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1. Target 5G visible light positioning signal subcarrier extraction method using particle swarm optimization algorithm

Author

Xun Zhang, Lina Shi, Yanqi Huang, El-Hassane Aglzim, Département de Recherche en Ingénierie des Véhicules pour l'Environnement [Nevers] (DRIVE), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB), Institut Supérieur d'Electronique de Paris (ISEP), Laboratoire d'Ingénierie des Systèmes de Versailles (LISV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), The authors gratefully acknowledge the financial support of the Bourgogne-Franche-Comte Region (Robassist project), and HUANG, Yanqi

Subjects
[SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic, Computer science, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], Visible Light Positioning(VLP), 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], Signal, Subcarrier, 0203 mechanical engineering, 0202 electrical engineering, electronic engineering, information engineering, Wireless, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Visible Light Communication(VLC), business.industry, particle swarm optimization(PSO), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 020208 electrical & electronic engineering, Frame (networking), Particle swarm optimization, 020302 automobile design & engineering, Received Signal Strength Indication(RSSI), [SPI.TRON] Engineering Sciences [physics]/Electronics, [SPI.TRON]Engineering Sciences [physics]/Electronics, Localization, Location-based service, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, Focus (optics), business, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 5G
Abstract

International audience; With the explosive growth of demand for Internet of Things (IoT) applications and the increasing dependence of users on wireless connections, indoor location based service(LBS) under 5G-Public-Private Partnership (5G-PPP) using cases have received more attention and get rapid development. Thanks to the safty, security and customization of 5G network pointed by 5G forum white paper, indoor positioning systems using unified 5G New Radio (NR) signals have become the focus of the next generation of visible light positioning (VLP) systems. In 5G New Radio(NR) frame, totally 192 subcarriers are used to carry positioning reference signal(PRS). In order to improve the position accuracy, the aim of this study is to investigate the method of the 5G NR subcarrier selection. A certain number of PRSs are obtained from the signal source of 192 subcarriers, and the minimum positioning error(PE) is calculated by using particle swarm optimization(PSO) algorithm. Then the relationship between the PRS with the minimum PE and the position of the subcarrier is analyzed which achieved the subcarrier extraction. The experimental results show that the average PE of this scheme is below 6 cm, and the minimum PE can reach 0.05 cm. This approach is confirmed the feasibility of high accuracy in 5G indoor VLP application and has the potential to be extended to larger and more complex 5G-PPP scenarios.

Published
2021

2. Single-view robot pose and joint angle estimation via render & compare

Author

Justin Carpentier, Josef Sivic, Mathieu Aubry, Yann Labbé, Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Models of visual object recognition and scene understanding (WILLOW), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, Czech Institute of Informatics, Robotics and Cybernetics [Prague] (CIIRC), Czech Technical University in Prague (CTU), This work was partially supported by the HPC resources from GENCI-IDRIS (Grant 011011181R1), the European Regional Development Fund under the project IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000468), Louis Vuitton ENS Chair on Artificial In-telligence, and the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Labbé, Yann, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID

Subjects
FOS: Computer and information sciences, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Context (language use), Degrees of freedom (mechanics), Synthetic data, Visualization, Computer Science - Robotics, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Articulated robot, Benchmark (computing), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Robot, Artificial intelligence, business, Robotics (cs.RO), Parametrization
Abstract

We introduce RoboPose, a method to estimate the joint angles and the 6D camera-to-robot pose of a known articulated robot from a single RGB image. This is an important problem to grant mobile and itinerant autonomous systems the ability to interact with other robots using only visual information in non-instrumented environments, especially in the context of collaborative robotics. It is also challenging because robots have many degrees of freedom and an infinite space of possible configurations that often result in self-occlusions and depth ambiguities when imaged by a single camera. The contributions of this work are three-fold. First, we introduce a new render & compare approach for estimating the 6D pose and joint angles of an articulated robot that can be trained from synthetic data, generalizes to new unseen robot configurations at test time, and can be applied to a variety of robots. Second, we experimentally demonstrate the importance of the robot parametrization for the iterative pose updates and design a parametrization strategy that is independent of the robot structure. Finally, we show experimental results on existing benchmark datasets for four different robots and demonstrate that our method significantly outperforms the state of the art. Code and pre-trained models are available on the project webpage https://www.di.ens.fr/willow/research/robopose/., Accepted at CVPR 2021 (Oral)

Published
2021

3. Subsequent Keyframe Generation for Visual Servoing

Author

Jocelyn Buisson, Nathan Crombez, Zhi Yan, Yassine Ruichek, Connaissance et Intelligence Artificielle Distribuées [Dijon] (CIAD), Université de Technologie de Belfort-Montbeliard (UTBM)-Université de Bourgogne (UB), and Crombez, Nathan

Subjects
Rest (physics), Service robot, 0209 industrial biotechnology, Computer science, business.industry, Generalization, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 02 engineering and technology, Object (computer science), Visual servoing, Automation, Visualization, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020901 industrial engineering & automation, Robustness (computer science), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Artificial intelligence, business
Abstract

International audience; In this paper, we study the problem of autonomous and reliable positioning of a camera w.r.t. an object when only this latter is known but not the rest of the scene. We propose to combine the advantages and efficiency of a visual servoing scheme and the generalization ability of a generative adversarial network. The paper describes how to efficiently create a synthetic dataset in order to train a network that predicts an intermediate visual keyframe between two images. Subsequent predictions are used as visual features to autonomously converge towards the desired pose even for large displacements. We show that the proposed method can be used without any prior knowledge on the scene appearance except for the object itself, while being robust to various lighting conditions and specular surfaces. We provide experimental results, both in simulation and using a real service robot platform to validate and evaluate the effectiveness, robustness, and accuracy of our approach.

Published
2021

4. Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation

Author

Hadrien Courtecuisse, Bernard Bayle, Paul Baksic, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), ANR-18-CE33-0007,SPERRY,CHIRURGIE ROBOTIQUE SUPERVISÉE - APPLICATION A L'ABLATION RADIOFREQUENCE DES TUMEURS DU FOIE(2018), Baksic, Paul, APPEL À PROJETS GÉNÉRIQUE 2018 - CHIRURGIE ROBOTIQUE SUPERVISÉE - APPLICATION A L'ABLATION RADIOFREQUENCE DES TUMEURS DU FOIE - - SPERRY2018 - ANR-18-CE33-0007 - AAPG2018 - VALID, Gestes Médico-Chirurgicaux Assistés par Ordinateur - - CAMI2011 - ANR-11-LABX-0004 - LABX - VALID, Computational Anatomy and Simulation for Medicine (MIMESIS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work was supported by French National Research Agency (ANR) within the project SPERRY ANR-18-CE33-0007 and the Investissements d'Avenir program (ANR-11-LABX-0004, Labex CAMI)., SOFA Framework, ANR-11-LABX-0004,CAMI,Gestes Médico-Chirurgicaux Assistés par Ordinateur(2011), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), and univOAK, Archive ouverte

Subjects
0209 industrial biotechnology, Computer science, Inverse, 02 engineering and technology, computer.software_genre, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 020901 industrial engineering & automation, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Projection (set theory), business.industry, Work (physics), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Inverse problem, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Finite element method, Visualization, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Virtual machine, Teleoperation, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer
Abstract

International audience; This paper deals with the problem of needle steering in deformable tissues subject to physiological motions. A novel shared control method is proposed, which combines an automatic needle steering algorithm with the motions applied by the radiologist, in order to place the needle tip at the desired location. The core motivation is to leave potentially dangerous decisions and actions to the practitioner, whereas complex nonintuitive manipulations of the needle are performed automatically, in particular to compensate for breathing motions. The most original part of the present work lies in the method used to combine user inputs with a closed-loop automatic needle steering control method based on inverse Finite Element simulations. The method is evaluated with a realistic virtual environment using 2D X-ray projection images. The results are compared with those obtained with a fully teleoperated system, on the one hand, and with a fully automatic solution, on the other hand. These experiments show that the shared control solution allows for a better needle tip placement when only projection imaging is available.

Published
2021

5. Exoscarne: Assistive Strategies for an Industrial Meat Cutting System Based on Physical Human-Robot Interaction

Author

Harsh Maithani, Youcef Mezouar, Matthieu Alric, L. Lequievre, Juan Antonio Corrales Ramon, Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Centro Singular de Investigacion en Tecnoloxias da Informacion (CiTIUS), Universidade de Santiago de Compostela [Spain] (USC ), Association pour le Développement de l'Institut de la Viande (ADIV), Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información, Universidade de Santiago de Compostela. Departamento de Electrónica e Computación, and CORRALES RAMON, Juan Antonio

Subjects
0209 industrial biotechnology, Technology, Collaborative robots, Meat packing industry, Computer science, QH301-705.5, QC1-999, Assistive robotics, Context (language use), 02 engineering and technology, Degrees of freedom (mechanics), Human–robot interaction, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 020901 industrial engineering & automation, Control theory, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], General Materials Science, Biology (General), Instrumentation, QD1-999, Fluid Flow and Transfer Processes, assistive robotics, Physical human-robot interaction, business.industry, Process Chemistry and Technology, Physics, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], General Engineering, collaborative robots, Engineering (General). Civil engineering (General), Computer Science Applications, Exoskeleton, Chemistry, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Proof of concept, Robot, 020201 artificial intelligence & image processing, TA1-2040, physical human-robot interaction, business
Abstract

Musculoskeletal disorders of the wrist are common in the meat industry. A proof of concept of a physical human-robot interaction (pHRI)-based assistive strategy for an industrial meat cutting system is demonstrated which can be transferred to an exoskeleton later. We discuss how a robot can assist a human in pHRI, specifically in the context of an industrial project i.e for the meat cutting industry. We developed an impedance control-based system that enables a KUKA LWR robot to provide assistive forces to a professional butcher while simultaneously allowing motion of the knife (tool) in all degrees of freedom. We developed two assistive strategies—a force amplification strategy and an intent prediction strategy—and integrated them into an impedance controller This research received funding from the French government research program Investissements d‘Avenir through the UMTs ACTIA Mécarnéo-AgRobErgo and the project Exoscarne (Call P3A-ICF2A-2I2A, FranceAgriMer) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 869855 (SoftManBot project) SI

Published
2021

6. Autonomous Flying With Neuromorphic Sensing

Author

Frederick D. Gregory, Chung-Chuan Lo, Ryad Benosman, Chou P. Hung, Patricia Parlevliet, Andrey Kanaev, Cynthia F. Moss, Andreas Schweiger, Yoram Gutfreund, Guido C. H. E. de Croon, Airbus [France], Naval Research Laboratory (NRL), US Army Research Laboratory-CIS Directorate (ARL), United States Army (U.S. Army), Airbus Group [Germany], Imperial College London, Institut de la Vision, Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Faculty of Aerospace Engineering [Delft], Delft University of Technology (TU Delft), Technion - Israel Institute of Technology [Haifa], Human Brain Research Center [Kyoto] (HBRC), Kyoto University [Kyoto], Johns Hopkins University (JHU), Gestionnaire, HAL Sorbonne Université 5, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Kyoto University

Subjects
Computer science, autonomous flight, Neurosciences. Biological psychiatry. Neuropsychiatry, flying animals, Computational resource, 03 medical and health sciences, 0302 clinical medicine, Software, Flight dynamics, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], energy efficiency, 030304 developmental biology, 0303 health sciences, Data processing, learning, business.industry, General Neuroscience, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Robotics, bio-inspiration, Neuromorphic engineering, Perspective, Systems engineering, Bird flight, Artificial intelligence, flight control, business, 030217 neurology & neurosurgery, neuromorphic sensing, RC321-571, Efficient energy use, Neuroscience
Abstract

International audience; Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.

Published
2021

7. OmniFlowNet: a Perspective Neural Network Adaptation for Optical Flow Estimation in Omnidirectional Images

Author

Charles-Olivier Artizzu, Guillaume Allibert, Cédric Demonceaux, Haozhou Zhang, Signal, Images et Systèmes (Laboratoire I3S - SIS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Equipe VIBOT - VIsion pour la roBOTique [ImViA EA7535 - ERL CNRS 6000] (VIBOT), Centre National de la Recherche Scientifique (CNRS)-Imagerie et Vision Artificielle [Dijon] (ImViA), Université de Bourgogne (UB)-Université de Bourgogne (UB), ANR-18-CE33-0004,CLARA,Couplage Apprentissage et Vision pour Contrôle de Robots Aeriens(2018), Allibert, Guillaume, and APPEL À PROJETS GÉNÉRIQUE 2018 - Couplage Apprentissage et Vision pour Contrôle de Robots Aeriens - - CLARA2018 - ANR-18-CE33-0004 - AAPG2018 - VALID

Subjects
Artificial neural network, Computer science, business.industry, Distortion (optics), Perspective (graphical), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Convolutional neural network, Convolution, Optical flow estimation, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Projection (set theory), 0105 earth and related environmental sciences
Abstract

International audience; Spherical cameras and the latest image processing techniques open up new horizons. In particular, methods based on Convolutional Neural Networks (CNNs) now give excellent results for optical flow estimation on perspective images. However, these approaches are highly dependent on their architectures and training datasets. This paper proposes to benefit from years of improvement in perspective images optical flow estimation and to apply it to omnidirectional ones without training on new datasets. Our network, OmniFlowNet, is built on a CNN specialized in perspective images. Its convolution operation is adapted to be consistent with the equirectangular projection. Tested on spherical datasets created with Blender 1 and several equirectangular videos realized from real indoor and outdoor scenes, OmniFlowNet shows better performance than its original network without extra training.

Published
2021

8. Cooperative Neighborhood Learning: Application to Robotic Inverse Model

Author

Bruno Dato, Frédéric Migeon, Marie-Pierre Gleizes, Systèmes Multi-Agents Coopératifs (IRIT-SMAC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, and DATO, Bruno

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], business.industry, Computer science, Multiagent Learning, Multi-agent system, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Control (management), Distributed Problem Solving, Robotics, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Kinematics, Space (commercial competition), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), Inverse Kinematics Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Constructivism (philosophy of education), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], Artificial intelligence, Learning by Endogenous Feedback, business, Robotic arm
Abstract

International audience; In this paper we present a generic multiagent learning system based on context learning applied in robotics. By applying learning with multiagent systems in robotics, we propose an endogenous self-learning strategy to improve learning performances. Inspired by constructivism, this learning mechanism encapsulates models in agents. To enhance the learning performance despite the weak amount of data, local and internal negotiation, also called cooperation, is introduced. Agents collaborate by generating artificial learning situations to improve their model. A second contribution is a new exploitation of the learnt models that allows less training. We consider highly redundant robotic arms to learn their Inverse Kinematic Model. A multiagent system learns a collective of models for a robotic arm. The exploitation of the models allows to control the end position of the robotic arm in a 2D/3D space. We show how the addition of artificial learning situations increases the performances of the learnt model and decreases the required labeled learning data. Experimentations are conducted on simulated arms with up to 30 joints in a 2D task space.

Published
2021

9. Surgical Tool Segmentation using Generative Adversarial Networks with Unpaired Training Data

Author

Florent Nageotte, Zhongkai Zhang, Benoit Rosa, ZHANG, Zhongkai, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Strasbourg (UNISTRA), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and univOAK, Archive ouverte

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Control and Optimization, Computer science, Biomedical Engineering, Inpainting, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Sciences de l'ingénieur [physics]/Automatique / Robotique, Task (project management), [SPI.AUTO]Engineering Sciences [physics]/Automatic, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Artificial Intelligence, unpaired data, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Segmentation, Pixel, business.industry, Mechanical Engineering, Surgical tool segmentation, Supervised learning, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Process (computing), deep learning, Pattern recognition, Image segmentation, Computer Science Applications, Human-Computer Interaction, [SPI.AUTO] Engineering Sciences [physics]/Automatic, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Control and Systems Engineering, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Task analysis, Computer Vision and Pattern Recognition, Artificial intelligence, generative adversarial networks, business
Abstract

International audience; Surgical tool segmentation is a challenging and crucial task for computer and robot-assisted surgery. Supervised learning approaches have shown great success for this task. However, they need a large number of paired training data. Based on Generative Adversarial Networks (GAN), unpaired image-toimage translation (I2I) techniques (like CycleGAN and dualGAN) have been proposed to avoid the requirement of paired data and have been employed for surgical tool segmentation. The unpaired I2I methods avoid annotating images for domain changes. Instead of using them directly for the segmentation task, we propose new GAN-based methods for unpaired I2I by embedding a specific constraint for segmentation, namely each pixel of input image belongs to either background or surgical tool. Our methods simplify the architectures of existing unpaired I2I with a reduced number of generators and discriminators. Compared with dualGAN, the proposed strategies have a faster training process without reducing the accuracy of the segmentation. Besides, we show that, using textured tool images as annotated samples to train discriminators, unpaired I2I (including our methods) can achieve simultaneous tool image segmentation and repair (such as reflection removal and tool inpainting). The proposed strategies are validated for image segmentation of a flexible tool and for in vivo images from the EndoVis dataset.

Published
2021

10. Forming a sparse representation for visual place recognition using a neurorobotic approach

Author

Guillaume Bresson, Olivier Romain, Sylvain Colomer, Nicolas Cuperlier, and Colomer, Sylvain

Subjects
FOS: Computer and information sciences, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, Computer Vision and Pattern Recognition (cs.CV), Pooling, Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Encoding (memory), 0502 economics and business, medicine, Code (cryptography), FOS: Electrical engineering, electronic engineering, information engineering, 050210 logistics & transportation, Artificial neural network, business.industry, 05 social sciences, Image and Video Processing (eess.IV), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.NEUR] Cognitive science/Neuroscience, Pattern recognition, Sparse approximation, Electrical Engineering and Systems Science - Image and Video Processing, Visualization, Visual cortex, medicine.anatomical_structure, Artificial intelligence, Neural coding, business
Abstract

This paper introduces a novel unsupervised neural network model for visual information encoding which aims to address the problem of large-scale visual localization. Inspired by the structure of the visual cortex, the model (namely HSD) alternates layers of topologic sparse coding and pooling to build a more compact code of visual information. Intended for visual place recognition (VPR) systems that use local descriptors, the impact of its integration in a bio-inpired model for self-localization (LPMP) is evaluated. Our experimental results on the KITTI dataset show that HSD improves the runtime speed of LPMP by a factor of at least 2 and its localization accuracy by 10%. A comparison with CoHog, a state-of-the-art VPR approach, showed that our method achieves slightly better results.

Published
2021

11. Leveraging Dynamic Occupancy Grids for 3D Object Detection in Point Clouds

Author

Ozgur Erkent, Jilles S. Dibangoye, David Sierra-Gonzalez, Anshul Paigwar, Christian Laugier, Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Université de Lyon-Institut National des Sciences Appliquées (INSA), Sierra-Gonzalez, David, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon, and Institut National des Sciences Appliquées (INSA)-Université de Lyon

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Occupancy grid mapping, Computer science, business.industry, Orientation (computer vision), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 010401 analytical chemistry, Feature extraction, Point cloud, 010501 environmental sciences, Object (computer science), 01 natural sciences, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Object detection, 0104 chemical sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Vehicle dynamics, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Minimum bounding box, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Artificial intelligence, business, 0105 earth and related environmental sciences
Abstract

International audience; Traditionally, point cloud-based 3D object detectors are trained on annotated, non-sequential samples taken from driving sequences (e.g. the KITTI dataset). However, by doing this, the developed algorithms renounce to exploit any dynamic information from the driving sequences. It is reasonable to think that this information, which is available at test time when deploying the models in the experimental vehicles, could have significant predictive potential for the object detection task. To study the advantages that this kind of information could provide, we construct a dataset of dynamic occupancy grid maps from the raw KITTI dataset and find the correspondence to each of the KITTI 3D object detection dataset samples. By training a Lidar-based state-of-the-art 3D object detector with and without the dynamic information we get insights into the predictive value of the dynamics. Our results show that having access to the environment dynamics improves by 27% the ability of the detection algorithm to predict the orientation of smaller obstacles such as pedestrians. Furthermore, the 3D and bird's eye view bounding box predictions for pedestrians in challenging cases also see a 7% improvement. Qualitatively speaking, the dynamics help with the detection of partially occluded and far-away obstacles. We illustrate this fact with numerous qualitative prediction results.

Published
2020

12. Depth-Adapted CNN for RGB-D cameras

Author

Christophe Stolz, Zongwei Wu, Guillaume Allibert, Cédric Demonceaux, Equipe VIBOT - VIsion pour la roBOTique [ImViA EA7535 - ERL CNRS 6000] (VIBOT), Centre National de la Recherche Scientifique (CNRS)-Imagerie et Vision Artificielle [Dijon] (ImViA), Université de Bourgogne (UB)-Université de Bourgogne (UB), Signal, Images et Systèmes (Laboratoire I3S - SIS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), ANR-18-CE33-0004,CLARA,Couplage Apprentissage et Vision pour Contrôle de Robots Aeriens(2018), Allibert, Guillaume, and APPEL À PROJETS GÉNÉRIQUE 2018 - Couplage Apprentissage et Vision pour Contrôle de Robots Aeriens - - CLARA2018 - ANR-18-CE33-0004 - AAPG2018 - VALID

Subjects
FOS: Computer and information sciences, Offset (computer science), Computer science, Computer Vision and Pattern Recognition (cs.CV), Coordinate system, Computer Science::Neural and Evolutionary Computation, Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Convolutional neural network, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Invariant (mathematics), business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 020207 software engineering, Weighting, Spatial coherence, Computer Science::Computer Vision and Pattern Recognition, RGB color model, Artificial intelligence, business, Linear filter
Abstract

Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt spatial sampling locations. The new model presented is invariant to scale and rotation around the X and the Y axis of the camera coordinate system. Moreover, when depth data is constant, our model is equivalent to a regular CNN. Experiments of benchmarks validate the effectiveness of our model., Accepted manuscript in ACCV 2020 (Oral)

Published
2020

13. 3D maps distribution of self-driving vehicles using roadside edges

Author

Hiroshi Esaki, Masaya Mizutani, Manabu Tsukada, Yuki Iida, Department of Creative Informatics, Graduate School of Information Science and Technology, the University of Tokyo (UTokyo), Department of Creative Informatics-Department of Creative Informatics, and Tsukada, Manabu

Subjects
0209 industrial biotechnology, Computer science, Real-time computing, Point cloud, Cloud computing, 02 engineering and technology, Upload, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, 020901 industrial engineering & automation, Software, 0203 mechanical engineering, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, Roadside edge, Server, 11. Sustainability, Bandwidth (computing), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], PCD map, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 020302 automobile design & engineering, Enhanced Data Rates for GSM Evolution, Cache, autonomous vehicles, business
Abstract

International audience; Three-dimensional (3D) maps have become a shared digital infrastructure for autonomous vehicles, especially in urban areas. Point Cloud Data (PCD) maps are used for scan matching to enable self-localization. Autonomous vehicles need to maintain PCD maps along with the destination that is often decided on demand and to keep the PCD map updated. In this paper, we propose a system that delivers PCD maps cached at roadside edges in real time. We implement the system in Autoware, an open-source software for autonomous driving. Subsequently, we evaluate whether the autonomous vehicle can simultaneously download the PCD map from its edge and enable self-localization. Our results show that autonomous vehicles can perform self-localization while downloading the PCD map from the edge server. Additionally, we measure the download time with variable bandwidth and examine the bandwidth in which the self-localization normally operates. In our results, the download time of the PCD map at 60 Mbps was 1.16 s at maximum, and it is indicated that 60 Mbps is the deadline for this system to work properly.

Published
2020

14. RobotDrlSim: A real time robot simulation platform for reinforcement learning and human interactive demonstration learning

Author

Xvdong Li, David Filliat, Zhaorun Chen, Liang Gong, Qizi Hu, Shenghan Xie, Te Sun, Shanghai Jiao Tong University [Shanghai], Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), This work was supported by National Natural Science Foundation of China (Grant No.51775333) and the Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.18391901000)., Filliat, David, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, History, Human interactive demonstration learning, Computer science, 02 engineering and technology, Education, 020901 industrial engineering & automation, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], computer.programming_language, Robot control, Deep reinforcement learning, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Robotics, Simulation platform, Python (programming language), Computer Science Applications, Interfacing, Benchmark (computing), Robot, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract

Deep reinforcement learning (DRL) techniques give robotics research an AI boost in many applications. In order to simultaneously accommodate the complex robotic behaviour simulation and DRL algorithm verification, a new simulation platform, namely the RobotDrlSim, is proposed. First, we design a standardized API interfacing mechanism for coordinating diverse environments on RobotDrlSim platform, where PyBullet simulator is equipped with an API to form a physical engine for robotics simulation. Second, benchmark DRL models are included in the baseline library for evaluation. Third, real-time human-robot interactions can be captured and imported to drive the RobotDrlSim tasks, which provide big data-stream for reinforcement learning. Experimentations show that cutting-edge DRL algorithms developed in python can be seamlessly deployed to the robots, and human interactions can be availed in training the robots. RobotDrlSim is valid for efficiently developing DRL algorithms for artificial intelligence models of robots, and it is especially suitable for the robot educational purposes.

Published
2020

15. Wearable wireless biosensors for spatiotemporal grip force profiling in real time

Author

Michel de Mathelin, Birgitta Dresp, Rongrong Liu, Florent Nageotte, Philippe Zanne, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche Contre les Cancers de l'Appareil Digestif-European Institute of Telesurgery (IRCAD/EITS), Centre National de la Recherche Scientifique (CNRS), Initiative D'EXcellence IDEX Université de Strasbourg, École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Dresp, Birgitta

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Knowledge representation and reasoning, Computer science, Wearable computer, simulator task, Human–robot interaction, Task (project management), functional analysis, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], human-robot interaction, wearable sensor technology, Human–computer interaction, Profiling (information science), robot assistance, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Wearable technology, human grip force, Signal processing, real time, image guided surgical training, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, spatio-temporal skill profiling, body regions, business, human activities, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Data transmission
Abstract

Biosensors and wearable sensor systems with transmitting capabilities are currently developed and used for the monitoring of health data, exercise activities, and other performance data. Unlike conventional approaches, these devices enable convenient, continuous, and/or unobtrusive monitoring of a user’s behavioral signals in real time. Examples include signals relative to hand movements and individual grip force data, which directly translate into spatiotemporal grip force profiles for the different measurement loci on the fingers and/or palm of the hand. Wearable sensor systems combine innovation in sensor design, electronics, data transmission, power management, and signal processing for statistical analysis, as will be shown in this presentation. The first part briefly summarizes current state of the art in grip force profiling to highlight important functional aspects. Then, wearable sensor technology in the form of sensor glove systems for the real-time monitoring of task skill evolution during training in a simulator task will be described on the basis of the spatiotemporal evolution of individual grip force profiles and their statistical and functional analysis. Although a lot of research is currently devoted to this area, many technological aspects still remain to be optimized, and new methods for data analysis and knowledge representation are urgently needed, as will be clarified in the discussion. Wearable sensor technology represents an open challenge for the scientific community and its further development partly relies on contributions from women researchers from multiple disciplines, as pointed out in the final conclusions of this presentation.

Published
2020

16. Real-Time Multi-SLAM System for Agent Localization and 3D Mapping in Dynamic Scenarios

Author

Hicham Hadj-Abdelkader, Fernando I. Ireta Munoz, Pierre Alliez, Samia Bouchafa, Bastien Rault, Jean-Yves Didier, Maxime Robin, David Roussel, Viachaslau Kachurka, Fabien Bonardi, Geometric Modeling of 3D Environments (TITANE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Innondura TB [Villeurbanne], and Ireta Muñoz, Fernando

Subjects
0209 industrial biotechnology, Inertial frame of reference, Computer science, Real-time computing, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wearable computer, 02 engineering and technology, Simultaneous localization and mapping, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 020901 industrial engineering & automation, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, business.industry, 3D reconstruction, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, [SPI.TRON] Engineering Sciences [physics]/Electronics, Visualization, [SPI.TRON]Engineering Sciences [physics]/Electronics, Lidar, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Global Positioning System, business, Geographic coordinate system, Monocular vision, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper introduces a Wearable SLAM system that performs indoor and outdoor SLAM in real time. The related project is part of the MALIN challenge which aims at creating a system to track emergency response agents in complex scenarios (such as dark environments, smoked rooms, repetitive patterns, building floor transitions and doorway crossing problems), where GPS technology is insufficient or inoperative. The proposed system fuses different SLAM technologies to compensate the lack of robustness of each, while estimating the pose individually. LiDAR and visual SLAM are fused with an inertial sensor in such a way that the system is able to maintain GPS coordinates that are sent via radio to a ground station, for real-time tracking. More specifically, LiDAR and monocular vision technologies are tested in dynamic scenarios where the main advantages of each have been evaluated and compared. Finally, 3D reconstruction up to three levels of details is performed.

Published
2020

17. Learning visual policies for building 3D shape categories

Author

Cordelia Schmid, Igor Kalevatykh, Ivan Laptev, Alexander Pashevich, Models of visual object recognition and scene understanding (WILLOW), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Service Expérimentation et Développement [Paris] (SED), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Pashevich, Alexander, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)

Subjects
FOS: Computer and information sciences, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, Computer Science - Machine Learning, Computer science, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, 010501 environmental sciences, Space (commercial competition), Machine learning, computer.software_genre, 01 natural sciences, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science - Robotics, 020901 industrial engineering & automation, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], State space, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Representation (mathematics), 0105 earth and related environmental sciences, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Construct (python library), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Real image, Object (computer science), Artificial Intelligence (cs.AI), Robot, Artificial intelligence, business, Classifier (UML), computer, Robotics (cs.RO)
Abstract

Manipulation and assembly tasks require non-trivial planning of actions depending on the environment and the final goal. Previous work in this domain often assembles particular instances of objects from known sets of primitives. In contrast, we aim to handle varying sets of primitives and to construct different objects of a shape category. Given a single object instance of a category, e.g. an arch, and a binary shape classifier, we learn a visual policy to assemble other instances of the same category. In particular, we propose a disassembly procedure and learn a state policy that discovers new object instances and their assembly plans in state space. We then render simulated states in the observation space and learn a heatmap representation to predict alternative actions from a given input image. To validate our approach, we first demonstrate its efficiency for building object categories in state space. We then show the success of our visual policies for building arches from different primitives. Moreover, we demonstrate (i) the reactive ability of our method to re-assemble objects using additional primitives and (ii) the robust performance of our policy for unseen primitives resembling building blocks used during training. Our visual assembly policies are trained with no real images and reach up to 95% success rate when evaluated on a real robot., IROS 2020

Published
2020

18. Indoor Localization and Mapping: Towards Tracking Resilience Through a Multi-SLAM Approach

Author

Bastien Rault, Jean-Yves Didier, Viachaslau Kachurka, Maxime Robin, David Roussel, Fernando I. Ireta Munoz, Samia Bouchafa, Hicham Hadj-Abdelkader, Fabien Bonardi, Pierre Alliez, Geometric Modeling of 3D Environments (TITANE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Innondura TB [Villeurbanne], ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Ireta Muñoz, Fernando, and 3IA Côte d'Azur - - 3IA@cote d'azur2019 - ANR-19-P3IA-0002 - P3IA - VALID

Subjects
0301 basic medicine, Computer science, Feature extraction, [INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR], Simultaneous localization and mapping, GPS signals, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Resilience (network), business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SPI.TRON] Engineering Sciences [physics]/Electronics, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Visualization, [INFO.INFO-ES] Computer Science [cs]/Embedded Systems, [SPI.TRON]Engineering Sciences [physics]/Electronics, 030104 developmental biology, Global Positioning System, Key (cryptography), Graph (abstract data type), [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

International audience; This paper presents a use case for SLAM techniques applied to real time localization and detailed mapping for emergency response personnel in non cooperative environments. Such environments tend to defeat conventional localization approaches, therefore we must ensure continuous operation of our localization and mapping regardless of the difficulties encountered (lack of GPS signals, lighting conditions, smoke, etc.). The proposed system fuses two SLAM algorithms, a LiDAR-based and a camera-based. Since LiDAR-based SLAM uses dense 3D measurements, it is well suited to the construction of a detailed map, while the visual SLAM allows to quickly recognize already visited places in order to apply loop closure corrections, by using a key frames graph. The currently proposed system allows collaboration between these two SLAMs through pose sharing and relocalization.

Published
2020

19. Trajectory Prediction of Traffic Agents: Incorporating context into machine learning approaches

Author

David Filliat, Vyshakh Palli-Thazha, Javier Ibanez-Guzman, Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Technocentre Renault [Guyancourt], RENAULT, Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Filliat, David

Subjects
Structure (mathematical logic), Artificial neural network, Process (engineering), business.industry, Computer science, 010401 analytical chemistry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Point cloud, Context (language use), 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Motion (physics), 0104 chemical sciences, 0202 electrical engineering, electronic engineering, information engineering, Trajectory, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, State (computer science), business, computer
Abstract

International audience; For a vehicle to navigate autonomously, it needs to perceive its surroundings and estimate the future state of the relevant traffic-agents with which it might interact as it navigates across public road networks. Predicting the future state of the perceived entities is a challenge, as these might appear to move in a stochastic manner. However, their motion is constrained to an extent by context, in particular the road network structure. Conventional machine learning methods are mainly trained using data from the perceived entities without considering roads, as a result trajectory prediction is difficult. In this paper, the notion of maps representing the road structure are included into the machine learning process. For this purpose, 3D LiDAR points and maps in the form of binary masks are used. These are used on a recurrent artificial neural network, the LSTM encoder-decoder based architecture to predict the motion of the interacting traffic agents. A comparison between the proposed solution with one that is only sensor driven (LiDAR) is included. For this purpose, NuScenes dataset is utilised, that includes annotated 3D point clouds. The results have demonstrated the importance of context to enhance our prediction performance as well as the capability of our machine learning framework to incorporate map information.

Published
2020

20. When Artificial Intelligence and Computational Neuroscience meet

Author

Philippe Gaussier, Benoît Girard, Peter Ford Dominey, Nicolas P. Rougier, Frédéric Alexandre, Mehdi Khamassi, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Architectures et modèles d'Adptation et de la cognition (AMAC), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Springer, Gaussier, Philippe, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, media_common.quotation_subject, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computational intelligence, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Perception, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Set (psychology), 030304 developmental biology, media_common, 0303 health sciences, Computational neuroscience, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.NEUR] Cognitive science/Neuroscience, Intelligent decision support system, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognition, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Salient, Robot, Artificial intelligence, business, 030217 neurology & neurosurgery, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences
Abstract

Computational Intelligence and Artificial Intelligence are both aiming at building machines and softwares capable of intelligent behavior. They are consequently prone to interactions, even if the latter is not necessarily interested in understanding how cognition emerges from the brain substrate. In this chapter, we enumerate, describe and discuss the most important fields of interactions. Some are methodological and are concerned with information representation, processing and learning. At the functional level, the focus is set on major cognitive functions like perception, navigation, decision making and language. Among the salient characteristics of the critical contributions of Computational Neuroscience to the development of intelligent systems, its systemic view of the cerebral functioning is particularly precious to model highly multimodal cognitive functions like decision making and language and to design cognitive architectures for the autonomous behavior of robots.

Published
2020

21. Motion Planning with Multi-Contact and Visual Servoing on Humanoid Robots

Author

Carlos Mastalli, Olivier Stasse, Gabriele Buondonno, Kevin Giraud-Esclasse, Pierre Fernbach, Équipe Mouvement des Systèmes Anthropomorphes (LAAS-GEPETTO), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and GIRAUD--ESCLASSE, Kévin

Subjects
Computer science, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Solver, Visual servoing, Computer Science::Robotics, Trajectory, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Differential dynamic programming, Artificial intelligence, Motion planning, business, Humanoid robot, Generator (mathematics)
Abstract

International audience; This paper describes the implementation of a canonical motion generation pipeline guided by vision for a TALOS humanoid robot. The proposed system is using a mul-ticontact planner, a Differential Dynamic Programming (DDP) algorithm, and a stabilizer. The multicontact planner provides a set of contacts and dynamically consistent trajectories for the Center-Of-Mass (CoM) and the Center-Of-Pressure (CoP). It provides a structure to initialize a DDP algorithm which, in turn, provides a dynamically consistent trajectory for all the joints as it integrates all the dynamics of the robot, together with rigid contact models and the visual task. Tested on Gazebo the resulting trajectory had to be stabilized with a state-of-the-art algorithm to be successful. In addition to testing motion generated from high specifications to the stabilized motion in simulation, we express visual features at Whole Body Generator level which is a DDP formulated solver. It handles non-linearities as the ones introduced by the projections of visual features expressed and minimized in the image plan of the camera.

Published
2020

22. PoseFusion: Dense RGB-D SLAM in Dynamic Human Environments

Author

Yoshihiko Nakamura, Tianwei Zhang, Nakamura Laboratory (Department of Mechano-Informatics), The University of Tokyo (UTokyo), Xiao J., Kröger T., Khatib O., and Zhang, Tianwei

Subjects
Computer science, business.industry, Deep learning, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Point cloud, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], RGB-D, Robotics, 02 engineering and technology, Simultaneous localization and mapping, Dynamic SLAM, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Feature (computer vision), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Graph (abstract data type), 020201 artificial intelligence & image processing, Segmentation, Computer vision, Artificial intelligence, business
Abstract

International audience; RGB-D Simultaneous Localization and Mapping (SLAM) in indoor environments is a hot topic in computer vision and robotics communities, and the dynamic environment is a remaining problem. Dynamic environments, which are often caused by dynamic humans in indoor environments, usually lead to the camera pose tracking method failure, feature association error or loop closure failure. In this paper, we propose a robust dense RGB-D SLAM method which efficiently detects humans and fast reconstructs the static backgrounds in the dynamic human environments. By using the deep learning-based human body detection method, we first quickly recognize the human body joints in the current RGB frame, even when the body is occluded. We then apply graph-based segmentation on the 3D point clouds, which separates the detected moving humans from the static environments. Finally, the left static environment is aligned with a state-of-the-art frame-to-model scheme. Experimental results on common RGB-D SLAM benchmark show that the proposed method achieves outstanding performance in dynamic environments. Moreover, it is even comparable to the performance of the related state-of-the-art methods in static environments.

Published
2020

23. A simple yet effective whole-body locomotion framework for quadruped robots

Author

Michele Focchi, Claudio Semini, Enrico Mingo Hoffman, Nikos G. Tsagarakis, Gennaro Raiola, Raiola, Gennaro, and Istituto Italiano di Tecnologia (IIT)

Subjects
0106 biological sciences, 0209 industrial biotechnology, Computer science, lcsh:Mechanical engineering and machinery, whole-body control, Context (language use), 02 engineering and technology, Kinematics, legged robots, locomotion framework, optimization, planning, 01 natural sciences, lcsh:QA75.5-76.95, Task (project management), 020901 industrial engineering & automation, Artificial Intelligence, Control theory, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], lcsh:TJ1-1570, Original Research, Robot locomotion, Robotics and AI, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Control engineering, Robotics, Gait, Computer Science Applications, Robot, lcsh:Electronic computers. Computer science, Artificial intelligence, business, 010606 plant biology & botany
Abstract

International audience; In the context of legged robotics, many criteria based on the control of the Center of Mass (CoM) have been developed to ensure stable and safe robot locomotion. Defining a whole-body framework with the control of the CoM requires a planning strategy, often based on a specific type of gait and reliable state-estimation. In a whole-body control approach, if the CoM task is not specified, the consequent redundancy can still be resolved by specifying a postural task that sets references for all the joints. Therefore, the postural task can be exploited to keep a well behaved, stable kinematic configuration. In this work, we propose a generic locomotion framework which is able to generate different kind of gaits, ranging from very dynamic gaits such as the trot, to more static gaits like the crawl, without the need to plan the CoM trajectory. Consequently, the whole-body controller becomes planner-free and it does not require the estimation of the floating base state, which is often prone to drift. The framework is composed of a priority-based whole-body controller that works in synergy with a walking pattern generator. We show the effectiveness of the framework by presenting simulations on different types of simulated terrains, including rough terrain, using different quadruped platforms.

Published
2020

24. Markerless 3D Human Pose Tracking in the Wild with fusion of Multiple Depth Cameras: Comparative Experimental Study with Kinect 2 and 3

Author

Jessica Colombel, François Charpillet, David Daney, Vincent Bonnet, Colombel, Jessica, M. A. R. Ahad et al., Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Augmenting human comfort in the factory using cobots (AUCTUS), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Polytechnique de Bordeaux (Bordeaux INP), Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), This work has been partly funded by the CPER IT2MP (Contrat Plan État Région, Innovations Technologiques, Modélisation Médecine Personnalisée) and FEDER (Fonds européen de développement régional)., Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut Polytechnique de Bordeaux (Bordeaux INP)-Inria Bordeaux - Sud-Ouest, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Computer science, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], 02 engineering and technology, Human motion, GeneralLiterature_MISCELLANEOUS, 03 medical and health sciences, Extended Kalman filter, 020901 industrial engineering & automation, 0302 clinical medicine, [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Tracking data, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Heuristics, business, Pose tracking, Merge (version control), 030217 neurology & neurosurgery
Abstract

International audience; Human-robot interaction requires a robust estimate of human motion in real-time. This work presents a fusion algorithm for joint center positions tracking from multiple depth cameras to improve human motion analysis accuracy. The main contribution is the proposed algorithm based on body tracking measurements fusion with an extended Kalman filter and anthropomorphic constraints, independent of sensors. As an illustration of the use of this algorithm, this paper presents the direct comparison of joint center positions estimated with a reference stereophotogrammetric system and the ones estimated with the new Kinect 3 (Azure Kinect) sensor and its older version the Kinect 2 (Kinect for Windows). The experiment was made in two parts, one for each model of Kinect, by comparing raw and merging body tracking data of two sided Kinect with the proposed algorithm. The proposed approach improves body tracker data for Kinect 3 which has not the same characteristics as Kinect 2. This study shows also the importance of defining good heuristics to merge data depending on how the body tracking works. Thus, with proper heuristics, the joint center position estimates are improved by at least 14.6 %. Finally, we propose an additional comparison between Kinect 2 and Kinect 3 exhibiting the pros and cons of the two sensors.

Published
2020

25. AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles

Author

Hiroshi Esaki, Mai Hirata, Akihide Ito, Manabu Tsukada, Takaharu Oi, Tsukada, Manabu, Department of Creative Informatics, Graduate School of Information Science and Technology, the University of Tokyo (UTokyo), Department of Creative Informatics-Department of Creative Informatics, and The University of Tokyo (UTokyo)

Subjects
0209 industrial biotechnology, Autonomous vehicle, Computer science, Realization (linguistics), 02 engineering and technology, Field (computer science), [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, 020901 industrial engineering & automation, Software, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Cooperative perception, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Open source software, Open-source software, 020202 computer hardware & architecture, Data sharing, Software deployment, Cooperative ITS, V2X, business
Abstract

International audience; The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autoware-based autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case.

Published
2020

26. Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study

Author

Guillaume Allibert, Ihab S. Mohamed, Philippe Martinet, Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe SYSTEMES, Signal, Images et Systèmes (Laboratoire I3S - SIS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA), Laboratoire des sciences et matériaux pour l'électronique et d'automatique (LASMEA), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Robots collaboratifs et hétérogènes interagissant dans des environnements vivants (CHORALE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), ANR-18-CE33-0004,CLARA,Couplage Apprentissage et Vision pour Contrôle de Robots Aeriens(2018), Allibert, Guillaume, APPEL À PROJETS GÉNÉRIQUE 2018 - Couplage Apprentissage et Vision pour Contrôle de Robots Aeriens - - CLARA2018 - ANR-18-CE33-0004 - AAPG2018 - VALID, Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Exploit, Computer science, Real-time computing, 02 engineering and technology, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Vehicle dynamics, Computer Science - Robotics, 020901 industrial engineering & automation, 0502 economics and business, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Observability, 050210 logistics & transportation, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 05 social sciences, Observable, Robotics, Grid, Trajectory, Task analysis, Artificial intelligence, business, Robotics (cs.RO)
Abstract

Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments., Comment: 8 pages, 8 figures, 3 tables

Published
2020
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27. Cooperative awareness using roadside unit networks in mixed traffic

Author

Hiroshi Esaki, Masahiro Kitazawa, Hideya Ochiai, Takaharu Oi, Manabu Tsukada, Department of Creative Informatics, Graduate School of Information Science and Technology, the University of Tokyo (UTokyo), Department of Creative Informatics-Department of Creative Informatics, University of Tokyo, Graduate School of Information Science and Technology, The University of Tokyo (UTokyo), and Tsukada, Manabu

Subjects
Scheme (programming language), cooperative ITS, Computer science, 010501 environmental sciences, Vehicle-to-vehicle, 01 natural sciences, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, Component (UML), 0502 economics and business, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], media_common.cataloged_instance, vehicle-to-vehicle, European union, Edge computing, 0105 earth and related environmental sciences, computer.programming_language, media_common, 050210 logistics & transportation, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], business.industry, Network packet, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 05 social sciences, cooperative awareness message, Grid, Software deployment, vehicular networking, standards, business, computer, Computer network
Abstract

International audience; Vehicle-to-vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. Although V2V standards have been specified by the European Union, United States, and Japan, the deployment phase represents mixed traffic in which connected and legacy vehicles co-exist. To enhance cooperative awareness in this mixed traffic, we assessed the special roadside unit that we developed in our previous work that generates required V2V messages on behalf of sensed target vehicles. In this paper, we extend our earlier work to propose a system called Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks. To minimize delay in message delivery, we designed the proposed system to use edge computing. The proposed scheme delivers cooperative messages to a wider area with a low delay and a high packet delivery ratio by prioritizing packets by their respective safety contributions. Our simulation results indicate that the proposed scheme efficiently delivers messages in heavy road traffic conditions modeled on real maps of Tokyo and Paris.

Published
2019

28. A Brief Survey on the Role of Dimensionality Reduction in Manipulation Learning and Control

Author

Pietro Falco, Sylvain Calinon, Fanny Ficuciello, and Calinon, Sylvain

Subjects
0209 industrial biotechnology, Control and Optimization, Computer science, Biomedical Engineering, 02 engineering and technology, Degrees of freedom (mechanics), Machine learning, computer.software_genre, Computer Science::Robotics, 03 medical and health sciences, 020901 industrial engineering & automation, 0302 clinical medicine, Artificial Intelligence, Robustness (computer science), Dimension (data warehouse), ComputingMilieux_MISCELLANEOUS, business.industry, Mechanical Engineering, Dimensionality reduction, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Linear subspace, Computer Science Applications, Human-Computer Interaction, Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

Bio-inspired designs are motivated by efficiency, adaptability and robustness of biological systems dynamic behaviors in complex environment. Despite progress in design, the lack of sensory-motor and learning capabilities is the main drawback of human-like manipulation systems. Dimensionality reduction has demonstrated in recent robotics research to solve problems that affect high degrees of freedom (DoFs) devices. In this paper, a survey on the role of dimensionality reduction in learning and control strategies is provided by discussing different techniques adopted for dimensionality reduction, as well as learning and control strategies built on subspaces of reduced dimension across different fully-actuated and underactuated anthropomorphic designs.

Published
2018

29. An efficient cooperative exploration strategy for wireless sensor network

Author

Assia Belbachir, Sorore Benabid, Belbachir, Assia, Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM), Université de Rouen Normandie (UNIROUEN), and Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, Computer science, Distributed computing, Computational Mechanics, Automotive industry, Markov process, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], symbols.namesake, 020901 industrial engineering & automation, Artificial Intelligence, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Cooperative strategy, Engineering (miscellaneous), ComputingMilieux_MISCELLANEOUS, business.industry, Mechanical Engineering, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Mobile robot, [INFO.INFO-ES] Computer Science [cs]/Embedded Systems, Cognitive radio, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], symbols, Robot, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, 020201 artificial intelligence & image processing, [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], business, Wireless sensor network
Abstract

Wireless sensor networks (WSNs) are used in several applications such as healthcare devices, aerospace systems, automobile industry, security monitoring. However, WSNs have several challenges to improve the efficiency, robustness, failure tolerance and reliability of these sensors. Thus, cooperation between sensors is an important deal that increases sensor trust. Cooperative WSNs can be used to optimize the exploration of an unknown area in a distributed way. In this paper, the distributed Markovian model strategy that is used due to their past state-dependent reasoning. Moreover, the exploration strategy depends totally on the wireless communication protocol. Hence, in this paper, we propose an efficient cooperative strategy based on cognitive radio and software-defined radio which are promising technologies that increase spectral utilization and optimize the use of radio resources. We implement a distributed exploration strategy (DES) in mobile robots, and several experiments have been performed to localize targets while avoiding obstacles. Experiments were performed with several exploration robots. A comparison with another exploration strategy shows that DES improves the robots exploration.

Published
2018

30. Wearable Robotics for Motion Assistance and Rehabilitation [TC Spotlight]

Author

Yasuhisa Hasegawa, Samer Mohammed, Qining Wang, Conor J. Walsh, Thomas G. Sugar, Juan Moreno, Nicola Vitiello, SIRIUS, Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Amirat, Yacine, Laboratoire Images, Signaux et Systèmes Intelligents ( LISSI ), and Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ) -Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 )

Subjects
Engineering, medicine.medical_treatment, 02 engineering and technology, 050601 international relations, Motion (physics), Wearable robot, Aeronautics, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Technical committee, Electrical and Electronic Engineering, ComputingMilieux_MISCELLANEOUS, Rehabilitation, business.industry, [ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 05 social sciences, Computer Science Applications1707 Computer Vision and Pattern Recognition, Robotics, Automation, Control and Systems Engineering, 0506 political science, Computer Science Applications, Artificial intelligence, business
Abstract

International audience; Reports on the Robotics and Automation Society (RAS) Technical Committee"s (TC) approval on Wearable Robotics during the IEEE RAS Technical Activities Board meeting that was held 15 May 2016 in Stockholm, Sweden, in conjunction with the IEEE International Conference on Robotics and Automation (ICRA) 2016.

Published
2018

31. Moments-Based Ultrasound Visual Servoing: From a Mono- to Multiplane Approach

Author

Alexandre Krupa, Christian Barillot, Jan Petr, Caroline Nadeau, Visual servoing in robotics, computer vision, and augmented reality (Lagadic), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Marchand, Eric

Subjects
0209 industrial biotechnology, Engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Visual servoing, Imaging phantom, Image (mathematics), 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], moment features, Segmentation, Computer vision, ultrasound images, Electrical and Electronic Engineering, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Ultrasound, Image plane, Object (computer science), Computer Science Applications, graph cut segmentation, Control and Systems Engineering, graph cut segmen- tation, 020201 artificial intelligence & image processing, Artificial intelligence, business, Servo
Abstract

International audience; This paper presents a new image-based visual servoing approach to control a robotic system equipped with an ultrasound imaging device. The presented method allows an automatic positioning of the probe with respect to an object of interest. Moments-based image features are computed from three orthogonal ultrasound images to servo in-plane and out-of-plane motions of the system. An efficient segmentation method, based on graph cut strategy, is proposed to extract the object contour in each image plane. Simulation results demonstrate that this approach improves upon techniques based on a single 2D US image in terms of probe positioning. Our method was also validated from robotic experiments performed on an ultrasound phantom with the use of a motorized 3D probe that provides the three US images.

Published
2016

32. A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot

Author

Luke Jai Wood, Costas S. Tzafestas, Farshid Amirabdollahian, Ben Robins, Kerstin Dautenhahn, Mehdi Khamassi, Gabriella Lakatos, Abolfazl Zaraki, University of Hertfordshire [Hatfield] (UH), Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI - USC 1361 INRAE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), University of Waterloo [Waterloo], Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (MITI ROBAUTISTE and PICS 279521), European Project: 687831,H2020,H2020-ICT-2015,BabyRobot(2016), Gestionnaire, HAL Sorbonne Université 5, and Child-Robot Communication and Collaboration: Edutainment, Behavioural Modelling and Cognitive Development in Typically Developing and Autistic Spectrum Children - BabyRobot - - H20202016-01-01 - 2018-12-31 - 687831 - VALID

Subjects
General Computer Science, Social Psychology, Process (engineering), media_common.quotation_subject, Autism, Control (management), 050105 experimental psychology, Human–robot interaction, 03 medical and health sciences, 0302 clinical medicine, Reinforcement learning, medicine, Autonomous robotics, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, Electrical and Electronic Engineering, Reinforcement learning algorithm, Reinforcement, media_common, Enthusiasm, business.industry, Teaching, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 05 social sciences, Robotics, medicine.disease, Human-Computer Interaction, Philosophy, Control and Systems Engineering, Robot, Children social skills, Artificial intelligence, business, Psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract

This paper presents a contribution aiming at testing novel child–robot teaching schemes that could be used in future studies to support the development of social and collaborative skills of children with autism spectrum disorders (ASD). We present a novel experiment where the classical roles are reversed: in this scenario the children are the teachers providing positive or negative reinforcement to the Kaspar robot in order for it to learn arbitrary associations between different toy names and the locations where they are positioned. The objective is to stimulate interaction and collaboration between children while teaching the robot, and also provide them tangible examples to understand that sometimes learning requires several repetitions. To facilitate this game, we developed a reinforcement learning algorithm enabling Kaspar to verbally convey its level of uncertainty during the learning process, so as to better inform the children about the reasons behind its successes and failures. Overall, 30 typically developing (TD) children aged between 7 and 8 (19 girls, 11 boys) and 9 children with ASD performed 25 sessions (16 for TD; 9 for ASD) of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. During the course of study Kaspar only made rare unexpected associations (2 perseverative errors and 2 win-shifts, within a total of 314 trials), primarily due to exploratory choices, and eventually reached minimal uncertainty. Thus, the robot’s behaviour was clear and consistent for the children, who all expressed enthusiasm in the experiment.

Published
2019

33. Combining Stochastic Optimization and Frontiers for Aerial Multi-Robot Exploration of 3D Terrains

Author

Alessandro Renzaglia, Olivier Simonin, Vincent Le Doze, Jilles Steeve Dibangoye, Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), and Renzaglia, Alessandro

Subjects
0209 industrial biotechnology, Mathematical optimization, Computer science, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Terrain, 02 engineering and technology, 020901 industrial engineering & automation, Local optimum, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], 0202 electrical engineering, electronic engineering, information engineering, Trajectory, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Stochastic optimization, Local search (optimization), [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], business
Abstract

International audience; This paper addresses the problem of exploring unknown terrains with a fleet of cooperating aerial vehicles. We present a novel decentralized approach which alternates gradient-free stochastic optimization and frontier-based approaches. Our method allows each robot to generate its trajectory based on the collected data and the local map built integrating the information shared by its team-mates. Whenever a local optimum is reached, which corresponds to a location surrounded by already explored areas, the algorithm identifies the closest frontier to get over it and restarts the local optimization. Its low computational cost, the capability to deal with constraints and the decentralized decision-making make it particularly suitable for multi-robot applications in complex 3D environments. Simulation results show that our approach generates feasible and safe trajectories which drive multiple robots to completely explore realistic environments. Furthermore, in terms of exploration time, our algorithm significantly outperforms a standard solution based on closest frontier points while providing similar performances compared to a computationally more expensive centralized greedy solution.

Published
2019

34. 3D Surface Reconstruction from Voxel-based Lidar Data

Author

Raoul de Charette, Luis Roldão, Anne Verroust-Blondet, Robotics & Intelligent Transportation Systems (RITS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AKKA Technologies, CIFRE AKKA Technologies, and Roldao Jimenez, Luis Guillermo

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Computer science, Computer Vision and Pattern Recognition (cs.CV), Gaussian, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, [INFO] Computer Science [cs], computer.software_genre, Computer Science - Robotics, symbols.namesake, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020901 industrial engineering & automation, Voxel, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Lidar data, Computer vision, [INFO]Computer Science [cs], ComputingMethodologies_COMPUTERGRAPHICS, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Kernel (image processing), symbols, Artificial intelligence, business, Robotics (cs.RO), computer, Surface reconstruction
Abstract

To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding. In this paper, a 3D surface reconstruction algorithm from heterogeneous density 3D data is presented. The proposed method is based on a TSDF voxel-based representation, where an adaptive neighborhood kernel sourced on a Gaussian confidence evaluation is introduced. This enables to keep a good trade-off between the density of the reconstructed mesh and its accuracy. Experimental evaluations carried on both synthetic (CARLA) and real (KITTI) 3D data show a good performance compared to a state of the art method used for surface reconstruction., IEEE Intelligent Transportation Systems Conference (ITSC) 2019

Published
2019

35. SparseCCL: Connected Components Labeling and Analysis for sparse images

Author

Arthur Marius Hennequin, Vladimir Gligorov, Lionel Lacassagne, Ben Couturier, Lacassagne, Lionel, Architecture et Logiciels pour Systèmes Embarqués sur Puce (ALSOC), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), European Organization for Nuclear Research (CERN), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), and Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], [INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], 02 engineering and technology, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], [SPI.AUTO]Engineering Sciences [physics]/Automatic, Image (mathematics), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Throughput (business), [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Sparse matrix, Connected component, 020203 distributed computing, Pixel, business.industry, [INFO.INFO-AO]Computer Science [cs]/Computer Arithmetic, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Process (computing), [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], [SPI.AUTO] Engineering Sciences [physics]/Automatic, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Computer Science::Computer Vision and Pattern Recognition, Benchmark (computing), [INFO.INFO-AO] Computer Science [cs]/Computer Arithmetic, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Decoding methods
Abstract

International audience; Connected components labeling and analysis for dense images have been extensively studied on a wide range of architectures. Some applications, like particles detectors in High Energy Physics, need to analyse many small and sparse images at high throughput. Because they process all pixels of the image, classic algorithms for dense images are inefficient on sparse data. We address this inefficiency by introducing a new algorithm specifically designed for sparse images. We show that we can further improve this sparse algorithm by specializing it for the data input format, avoiding a decoding step and processing multiple pixels at once. A benchmark on Intel and AMD CPUs shows that the algorithm is from ×1.6 to ×2.5 faster on sparse images.

Published
2019

36. Automatic Self-Calibration of Suspended Under-Actuated Cable-Driven Parallel Robot using Incremental Measurements

Author

Jean-Pierre Merlet, Marco Carricato, Edoardo Idà, Dipartimento di Scienze dell'Informazione [Bologna] (DISI), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), HExapode, PHysiologie, AssISTance et Objets de Service (HEPHAISTOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Dipartimento di Informatica - Scienza e Ingegneria [Bologna] (DISI), ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Andreas Pott, Tobias Bruckmann, Ida E., Merlet J.-P., Carricato M., Merlet, Jean-Pierre, and 3IA Côte d'Azur - - 3IA@cote d'azur2019 - ANR-19-P3IA-0002 - P3IA - VALID

Subjects
0209 industrial biotechnology, Forward kinematics, business.product_category, Optimization problem, Computer science, Homing, 02 engineering and technology, Underconstrained robots, Pulley, Computer Science::Robotics, 020901 industrial engineering & automation, Data acquisition, 0203 mechanical engineering, Control theory, Initial value problem, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Pose, Underactuated robots, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Cable-driven parallel robot, Underactuated robot, Parallel manipulator, Cable-driven parallel robots, 020303 mechanical engineering & transports, Robot, Self-calibration, business
Abstract

International audience; This paper focuses on the problem of the initial-pose estimation by means of proprioceptive sensors (self-calibration) of suspended under-actuated Cable-Driven Parallel Robots (CDPRs). For this class of manipulators, the initial-pose estimation cannot be carried out by means of forward kinematics only, but mechanical equilibrium conditions must be considered as well. In addition , forward kinematics solution is based on cable-length measurements, but if the robot is equipped with incremental sensors cables' initial values are unknown. In this paper, the self-calibration problem is formulated as a non-linear least square optimization problem (NLLS), based on the direct geometrico-static problem, where only incremental measurements on cable lengths and on swivel pulley angles are required. In addition, a data acquisition algorithm and an initial value selection procedure for the NLLS are proposed, aiming at automatizing the self-calibration procedure. Simulations and experimental results on a 3-cable 6-degree-of-freedom robot are provided so as to prove the effectiveness of the proposed methodology.

Published
2019

37. Attentional PointNet for 3D-Object Detection in Point Clouds

Author

Christian Wolf, Anshul Paigwar, Ozgur Erkent, Christian Laugier, Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria), and Paigwar, Anshul

Subjects
Computer science, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Point cloud, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Inference, Scale (descriptive set theory), 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Object detection, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Lidar, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Representation (mathematics), ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

International audience; Accurate detection of objects in 3D point clouds is a central problem for autonomous navigation. Most existing methods use techniques of hand-crafted features representation or multi-sensor approaches prone to sensor failure. Approaches like PointNet that directly operate on sparse point data have shown good accuracy in the classification of single 3D objects. However, LiDAR sensors on Autonomous Vehicles generate a large scale point cloud. Real-time object detection in such a cluttered environment still remains a challenge. In this study, we propose Attentional Point- Net, which is a novel end-to-end trainable deep architecture for object detection in point clouds. We extend the theory of visual attention mechanisms to 3D point clouds and introduce a new recurrent 3D Localization Network module. Rather than processing the whole point cloud, the network learns where to look (finding regions of interest), which significantly reduces the number of points to be processed and inference time. Evaluation on KITTI car detection benchmarkshows that our Attentional PointNet achieves comparable results with the state-of-the-art LiDAR-based 3D detection methods in detection and speed.

Published
2019

38. Using Comanipulation with Active Force Feedback to Undistort Stiffness Perception in Laparoscopy

Author

Ignacio Avellino, Olivier Piccin, Bernard Bayle, Guillaume Morel, Jimmy Da Silva, Josue Sulub, Laurent Barbé, François G. Schmitt, Université de Strasbourg (UNISTRA), Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance aux Gestes et Applications THErapeutiques (AGATHE), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Avellino, Ignacio

Subjects
Laparoscopic surgery, 0209 industrial biotechnology, Lever, business.product_category, 020205 medical informatics, medicine.diagnostic_test, Computer science, medicine.medical_treatment, media_common.quotation_subject, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Informatique [cs]/Robotique [cs.RO], Stiffness, 02 engineering and technology, 020901 industrial engineering & automation, Active force, Perception, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], medicine.symptom, business, Laparoscopy, Simulation, ComputingMilieux_MISCELLANEOUS, media_common
Abstract

Surgeons performing laparoscopic surgery expe-rience distortion when perceiving the stiffness of a patient’s tis-sues. This is due to the lever effect induced by the introductionof instruments in their patient’s body through a fulcrum. Toaddress this problem, we propose to use the comanipulationparadigm. A robotic device is connected to the handle of theinstrument while simultaneously being held by the surgeon.This device applies a force on the handle that reflects the forcemeasured at the tool tip, with a gain that depends on the leverratio. The implementation of this method is presented on anexperimental setup and a preliminary assessment experimentis presented.

Published
2019

39. Stochastic DP Based on Trained Database for Sub-optimal Energy Management of Hybrid Electric Vehicles

Author

Rustem Abdrakhmanov, Lounis Adouane, Institut Pascal (IP), SIGMA Clermont (SIGMA Clermont)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), and Adouane, Lounis

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], 0209 industrial biotechnology, Optimization problem, business.product_category, Computer science, Energy management, 02 engineering and technology, computer.software_genre, Energy minimization, Multi-objective optimization, [SPI.AUTO]Engineering Sciences [physics]/Automatic, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0502 economics and business, Electric vehicle, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], MATLAB, ComputingMilieux_MISCELLANEOUS, computer.programming_language, 050210 logistics & transportation, Database, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 05 social sciences, Stochastic programming, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Markov decision process, business, [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering, computer
Abstract

This paper presents a sub-optimal energy management strategy, based on Stochastic Dynamic Programming (SDP), for efficient powersplit of a Hybrid Electric Vehicle (HEV). An optimal energy management strategy is proposed, permitting to have simultaneous speed profile and powersplit optimization of the HEV. Formulated as a multi-objective optimization problem, an \(\epsilon \)-constraint method has been used to find the Pareto front of the energy optimization task. Traffic conditions and driver behavior could be assimilated to a stochastic nature, thus, it is proposed in this paper to address the vehicle power as Markov Decision Process. A Stochastic Database is used to store Transition Probability and Reward Matrices, corresponding to suitable vehicle actions w.r.t. specific states. They are used afterwards to calculate sub-optimal powersplit policy for the vehicle via an infinite-horizon SDP approach. Simulation results demonstrate the effectiveness of the proposed approach compared to a deterministic strategy given in [1]. The present work is conducted on a dedicated high-fidelity model of the HEV that was developed on MATLAB/TruckMaker software.

Published
2019

40. On-line and on-board planning and perception for quadrupedal locomotion

Author

Alexander W. Winkler, Claudio Semini, Carlos Mastalli, Darwin G. Caldwell, Ioannis Havoutis, and Mastalli, Carlos

Subjects
FOS: Computer and information sciences, business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Computer Science - Computer Vision and Pattern Recognition, Body movement, On board, Computer Science - Robotics, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Quadrupedalism, Action plan, Perception, Motion planning, Artificial intelligence, business, Robotics (cs.RO), media_common
Abstract

We present a legged motion planning approach for quadrupedal locomotion over challenging terrain. We decompose the problem into body action planning and footstep planning. We use a lattice representation together with a set of defined body movement primitives for computing a body action plan. The lattice representation allows us to plan versatile movements that ensure feasibility for every possible plan. To this end, we propose a set of rules that define the footstep search regions and footstep sequence given a body action. We use Anytime Repairing A* (ARA*) search that guarantees bounded suboptimal plans. Our main contribution is a planning approach that generates on-line versatile movements. Experimental trials demonstrate the performance of our planning approach in a set of challenging terrain conditions. The terrain information and plans are computed on-line and on-board., 7 pages, International Conference on Technologies for Practical Robot Applications

Published
2019

41. Using MAP-Elites to Optimize Self-Assembling Behaviors in a Swarm of Bio-micro-robots

Author

Nathanael Aubert-Kato, Nicolas Bredeche, Leo Cazenille, Bredeche, Nicolas, Institut des Systèmes Intelligents et de Robotique (ISIR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, Computer Science::Neural and Evolutionary Computation, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 02 engineering and technology, Space (commercial competition), [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Robotics, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Self assembling, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], swarm robotics, 030304 developmental biology, Structure (mathematical logic), 0303 health sciences, Bio-micro-robots, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Swarm behaviour, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], molecular programming, MAP-Elites, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Robot, quality-diversity algorithms, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], business, evolutionary robotics
Abstract

We are interested in programming a swarm of molecular robots that can perform self-assembly to form specific shapes at a specific location. Programming such robot swarms is challenging for two reasons. First, the goal is to optimize both the parameters and the structure of chemical reaction networks. Second, the search space is both high-dimensional and deceptive. In this paper, we show that MAP-Elites, an algorithm that searches for both high-performing and diverse solutions, outperforms previous state-of-the-art optimization methods.

Published
2019

42. Estimating the reliability of georeferenced lane markings for map-aided localization

Author

Philippe Xu, Clement Zinoune, Anthony Welte, Philippe Bonnifait, Welte, Anthony, Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Renault SAS, Technocentre Renault [Guyancourt], and RENAULT-RENAULT

Subjects
050210 logistics & transportation, 0209 industrial biotechnology, Computer science, business.industry, 05 social sciences, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Process (computing), Likelihood maximization, 02 engineering and technology, 020901 industrial engineering & automation, Georeference, 0502 economics and business, Trajectory, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Kalman smoothing, Artificial intelligence, business, Reliability (statistics)
Abstract

International audience; Maps can greatly improve vehicle localization using perception sensors that detect features georeferenced in the map. This relies on two assumptions. Firstly, the detected features and the elements of the map have to be correctly associated. Secondly, the features of the map have to be accurately referenced. In this paper, solutions regarding these issues are presented. The case study of localization using a camera detecting road markings is considered. A Kalman smoothing process is used to obtain the best possible estimate of the trajectory that enables to evaluate the reliability of markings stored in the map. A likelihood maximization technique is used to best associate the observed markings to those referenced in the map. By using these two methods, map errors are detected after a first passage in an area and can be mitigated in later passes. Experimental results are reported to evaluate the performance of this approach. It is shown that mapping errors can be correctly handled.

Published
2019

43. Evaluation of PAEXO, a novel passive exoskeleton for overhead work

Author

Serena Ivaldi, Jonas Bornmann, Lars Fritzsche, Claudia Latella, Jernej Čamernik, Dasa Gorjan, Jan Babič, Pauline Maurice, Benjamin Schirrmeister, Daniele Pucci, Luca Tagliapietra, Maurice, Pauline, Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Jozef Stefan Institute [Ljubljana] (IJS), Ottobock SE & Co. KGaA, Istituto Italiano di Tecnologia (IIT), and imk automotive GmbH

Subjects
Computer science, Biomedical Engineering, Bioengineering, 03 medical and health sciences, Upper limb exoskeleton, 0302 clinical medicine, upper-limb exoskeleton, technology acceptance, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Overhead (computing), 0501 psychology and cognitive sciences, field-testing, 050107 human factors, ComputingMilieux_MISCELLANEOUS, business.industry, 05 social sciences, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], General Medicine, 030210 environmental & occupational health, 3. Good health, Computer Science Applications, Exoskeleton, Human-Computer Interaction, Work (electrical), Embedded system, Physiological measures, business, workload assessment
Abstract

Work-related musculoskeletal disorders (WMSDs) are the first cause of occupational diseases in developed countries and represent a major health issue and an important cost for companies (Parent-Thi...

Published
2019

44. Polarized skylight-based heading measurements: a bio-inspired approach

Author

Julien Dupeyroux, Stéphane Viollet, Julien Serres, Dupeyroux, Julien, Réseau national de plateformes robotiques d'excellence - - ROBOTEX2010 - ANR-10-EQPX-0044 - EQPX - VALID, Institut des Sciences du Mouvement Etienne Jules Marey (ISM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS), ANR-10-EQPX-0044,ROBOTEX,Réseau national de plateformes robotiques d'excellence(2010), and Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)

Subjects
Heading (navigation), Insecta, Light, Non-conventional vision, Biomedical Engineering, Biophysics, Bioengineering, Biochemistry, Astronomical instrumentation, law.invention, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Biomaterials, 03 medical and health sciences, 0302 clinical medicine, Optics, Polarization vision, law, Compass, Animals, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Vision, Ocular, Life Sciences–Engineering interface, 030304 developmental biology, Physics, 0303 health sciences, Linear polarization, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Robotics, Polarizer, Skylight, Polarization (waves), [SPI.TRON] Engineering Sciences [physics]/Electronics, [SPI.TRON]Engineering Sciences [physics]/Electronics, Atmospheric optics, Spectral sensitivity, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Bio-inspiration, business, 030217 neurology & neurosurgery, Spatial Navigation, Biotechnology
Abstract

Many insects such as desert ants, crickets, locusts, dung beetles, bees and monarch butterflies have been found to extract their navigation cues from the regular pattern of the linearly polarized skylight. These species are equipped with ommatidia in the dorsal rim area of their compound eyes, which are sensitive to the angle of polarization of the skylight. In the polarization-based robotic vision, most of the sensors used so far comprise high-definition CCD or CMOS cameras topped with linear polarizers. Here, we present a 2-pixel polarization-sensitive visual sensor, which was strongly inspired by the dorsal rim area of desert ants' compound eyes, designed to determine the direction of polarization of the skylight. The spectral sensitivity of this minimalistic sensor, which requires no lenses, is in the ultraviolet range. Five different methods of computing the direction of polarization were implemented and tested here. Our own methods, theextendedandAntBotmethod, outperformed the other three, giving a mean angular error of only 0.62° ± 0.40° (median: 0.24°) and 0.69° ± 0.52° (median: 0.39°), respectively (mean ± standard deviation). The results obtained in outdoor field studies show that our celestial compass gives excellent results at a very low computational cost, which makes it highly suitable for autonomous outdoor navigation purposes.

Published
2019

45. Human-Like Decision-Making for Automated Driving in Highways

Author

Jilles S. Dibangoye, David Sierra Gonzalez, Mario Garzón, Christian Laugier, Sierra-Gonzalez, David, Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université de Lyon-Institut National des Sciences Appliquées (INSA)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, Monte Carlo tree search, Control (management), 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0502 economics and business, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Leverage (statistics), 050207 economics, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, business.industry, 05 social sciences, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Partially observable Markov decision process, Statistical model, Search tree, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Action (philosophy), Artificial intelligence, business, computer
Abstract

In this work, we present a decision-making system for automated vehicles driving in highway environments. The task is modeled as a Partially Observable Markov Decision Process, in which the physical states and intentions of surrounding traffic are uncertain. The problem is solved in an online fashion using Monte Carlo tree search. At each decision step, a search tree of beliefs is incrementally built and explored in order to find the current best action for the ego-vehicle. The beliefs represent the predicted state of the world as a response to the actions of the ego-vehicle and are updated using an interaction-and intention-aware probabilistic model. To estimate the long-term consequences of any action, we rely on a lightweight model-based prediction of the scene that assumes risk-averse behavior for all agents. We refer to the proposed decision-making approach as human-like, since it mimics the human abilities of anticipating the intentions of surrounding drivers and of considering the long-term consequences of their actions based on an approximate, common-sense, prediction of the scene. We evaluate the proposed approach in two different navigational tasks: lane change planning and longitudinal control. The results obtained demonstrate the ability of the proposed approach to make foresighted decisions and to leverage the uncertain intention estimations of surrounding drivers.

Published
2019

46. Position and Velocity Control for Telemanipulation with Interoperability Protocol

Author

Kaspar Althoefer, Stefan Poslad, Ildar Farkhatdinov, Francesca Palermo, Maurizio Valle, Bukeikhan Omarali, and Farkhatdinov, Ildar

Subjects
0209 industrial biotechnology, Telerobotics, Computer science, business.industry, 020208 electrical & electronic engineering, Interoperability, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Control engineering, Robotics, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 02 engineering and technology, Workspace, 020901 industrial engineering & automation, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Data exchange, Teleoperation, 0202 electrical engineering, electronic engineering, information engineering, Robot, Artificial intelligence, business, Protocol (object-oriented programming), [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering
Abstract

In this paper we describe how a generic interoperability teler-obotics protocol can be applied for master-slave robotic systems operating in position-position, position-speed and hybrid control modes. The interoperability protocol allows robust and efficient data exchange for teleoperation systems, however it was not shown how it can fit switching position and rate control modes. Here we propose the general framework of hybrid position and rate control modes with interoperability protocol. Furthermore, we demonstrate experimentally that the framework is suitable for robotics teleoperation systems in which a human-operator can switch between position-position and position-speed master and slave robots' workspace mapping.

Published
2019

47. Laser-Supported Monocular Visual Tracking for Natural Environments

Author

Georges Chahine, Cédric Pradalier, Pradalier, Cedric, Georgia Tech Lorraine [Metz], Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Georgia Institute of Technology [Atlanta]-CentraleSupélec-Ecole Nationale Supérieure des Arts et Metiers Metz-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Monocular, Computer science, business.industry, Reliability (computer networking), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Scale (descriptive set theory), 02 engineering and technology, Laser, Natural (archaeology), law.invention, Constraint (information theory), 020901 industrial engineering & automation, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], law, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Eye tracking, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Set (psychology), business
Abstract

This paper presents and demonstrates a 2D laser-supported visual tracking solution, that can achieve reliable performance in unstructured scenes such as those seen in natural environment surveys. The method is shown to sufficiently stabilize scale and account for scale drift, as well as improve overall reliability. The suggested method is minimally invasive, does not require any additional parameter and does not necessarily require a laser, which can be replaced by any set of points with known depth, with no constraint on the temporal continuity of known points. We also test our method on 4 surveys, captured in a natural riverine environment, that proved to be challenging even for the state-of-the-art in visual tracking.

Published
2019

48. AI-IMU Dead-Reckoning

Author

Axel Barrau, Silvere Bonnabel, Martin Brossard, Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Safran Tech, Institut de sciences exactes et appliquées (ISEA), Université de la Nouvelle-Calédonie (UNC), and BUNC, Pole ID

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Control and Optimization, Computer science, KITTI dataset, Context (language use), Machine Learning (stat.ML), 02 engineering and technology, invariant extended Kalman filter, 01 natural sciences, localization, Invariant extended Kalman filter, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Computer Science - Robotics, 020901 industrial engineering & automation, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Odometry, Statistics - Machine Learning, Artificial Intelligence, Inertial measurement unit, Dead reckoning, inertial navigation, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, ComputingMilieux_MISCELLANEOUS, Inertial navigation system, business.industry, inertial measurement unit, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 010401 analytical chemistry, deep learning, Filter (signal processing), Kalman filter, 0104 chemical sciences, Automotive Engineering, Artificial intelligence, business, Robotics (cs.RO)
Abstract

International audience; In this paper we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometry dataset, and our dead-reckoning inertial method based only on the IMU accurately estimates 3D position, velocity, orientation of the vehicle and self-calibrates the IMU biases. We achieve on average a 1.10% translational error and the algorithm competes with top-ranked methods which, by contrast, use LiDAR or stereo vision. We make our implementation open-source at: https://github.com/mbrossar/ai-imu-dr

Published
2019
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49. Smart walkers: an application-oriented review

Author

Ludovic Saint-Bauzel, P. Rumeau, Solenne Page, Viviane Pasqui, Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Gérontopôle, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Epidémiologie et Analyses en Santé Publique : risques, maladies chroniques et handicap (LEASP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Saint-Bauzel, Ludovic

Subjects
[INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], 0209 industrial biotechnology, Computer science, General Mathematics, 02 engineering and technology, GeneralLiterature_MISCELLANEOUS, 020901 industrial engineering & automation, Human–computer interaction, Smart walker, Evaluation methods, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], 0202 electrical engineering, electronic engineering, information engineering, Mechanical design, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Modular design, [INFO.INFO-ES] Computer Science [cs]/Embedded Systems, Computer Science Applications, Control and Systems Engineering, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, 020201 artificial intelligence & image processing, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, Software
Abstract

SUMMARYIn this paper, a development method for smart walker prototypes is proposed. Development of such prototypes is based on technological choices and device evaluations. The method is aimed at guiding technological choices in a modular fashion. First, the method for choosing modules to be integrated in a smart walker is presented. Application-specific modules are then studied. Finally, the issues of evaluation are investigated. In order to work out this method, more than 50 smart walkers and their pros and cons with respect to the different studied applications are reviewed.

Published
2016

50. Audio-Motor Integration for Robot Audition

Author

Alexander Schmidt, Walter Kellermann, Antoine Deleforge, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), and Deleforge, Antoine

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Context (language use), computer.software_genre, 01 natural sciences, Field (computer science), ego-noise reduction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Human–computer interaction, 0103 physical sciences, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Audio signal processing, 010301 acoustics, audio-motor integration, robot audition, single-channel, Modality (human–computer interaction), business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Robotics, sound localization, multi-channel, Pipeline (software), Noise, Robot, Artificial intelligence, business, dictionary learning, computer, 030217 neurology & neurosurgery
Abstract

International audience; In the context of robotics, audio signal processing in the wild amounts to dealing with sounds recorded by a system that moves and whose actuators produce noise. This creates additional challenges in sound source localization, signal enhancement and recognition. But the speci-ficity of such platforms also brings interesting opportunities: can information about the robot actuators' states be meaningfully integrated in the audio processing pipeline to improve performance and efficiency? While robot audition grew to become an established field, methods that explicitly use motor-state information as a complementary modality to audio are scarcer. This chapter proposes a unified view of this endeavour, referred to as audio-motor integration. A literature review and two learning-based methods for audio-motor integration in robot audition are presented, with application to single-microphone sound source localization and ego-noise reduction on real data.

Published
2018

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