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Comunicación a las Jornadas (Este año, bajo el nombre de Jornadas de Robótica, Educación en Automática y Bioingeniería se celebran, de forma conjunta las XII Jornadas Nacionales de Robótica, el VIII Seminario de Educación en Automática, y el XIII Simposio CEA de Bioingeniería.) En este artículo se presenta un método para el cálulo de la odometría en tiempo real de una cámara de profundidad, tipicamente una cámara RGB-D, donde solo se emplea la imagen D. El método propuesto tiene la ventaja frente a las alternativas existentes en el estado del arte de ser eficiente a la vez que ofrece una precisión superior. Para ello, la estimación del movimiento entre dos imágenes de entrada se calcula minimizando la distancia entre trozos planos de una imagen y puntos de la otra. La propuesta incluye un procedimiento iterativo que refina la solución (trasformación rígida entre dos imágenes consecutivas) mediante la actualización de los emparejamientos de pares de puntos y planos característicos hasta la convergencia. El método presentado se ha evaluado y comparado con una técnica del estado del arte, mostrando una reducción del 25% de la mediana de los errores de translación y rotación, funcionando a la misma frecuencia (~30Hz). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.

This article deals with the problem of room categorization, i.e. the classification of a room as being a bathroom, kitchen, living-room, bedroom, etc., by an autonomous robot operating in home environments. For that, we propose a room categorization system based on a Bayesian probabilistic framework that combines object detections and its semantics. For detecting objects we resort to a state-of-the-art CNN, Mask R-CNN, while the meaning or semantics of those detections is provided by an ontology. Such an ontology encodes the relations between object and room categories, that is, in which room types the different object categories are typically found (toilets in bathrooms, microwaves in kitchens, etc.). The Bayesian framework is in charge of fusing both sources of information and providing a probability distribution over the set of categories the room can belong to. The proposed system has been evaluated in houses from the Robot@Home dataset, validating its effectiveness under real-world conditions.

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform. This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga.

Data used for evaluation in: "A Framework for the Calibration of Systematic Errors in Depth Cameras on Mobile Robots" Both the calibration sequence (2019-03-15-12-43-44.bag) and the evaluation sequence (2019-03-15-13-10-09.bag) are available. For more information, please visit: github.com/dzunigan/depth_calibration, For more information, please visit: https://github.com/dzunigan/depth_calibration

Proyecto de Excelencia Junta de Andalucía TEP2012-530 Olfaction is a valuable source of information about the environment that has not been su ciently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efciency, autonomy and usefulness. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

This article reports on the EU project ExCITE with specific focus on the technical development of the telepresence platform over a period of 42 months. The aim of the project was to assess the robustness and validity of the mobile robotic telepresence (MRP) system Giraff as a means to support elderly people and to foster their social interaction and participation. Embracing the idea of user-centered product refinement, the robot was tested over long periods of time in real homes. As such, the system development was driven by a strong involvement of elderly people and their caregivers but also by technical challenges associated with deploying the robot in real-world contexts. The results of the 42-months’ long evaluation is a system suitable for use in homes rather than a generic system suitable, for example, in office environments., Funding Agency:EU AAL-2009-2-125, ExCITE

[Resumen] En este trabajo se propone un método que combina descriptores de imágenes de intensidad y de profundidad para detectar de manera robusta el problema de cierre de bucle en SLAM. La robustez del método, proporcionada por el empleo conjunto de información de diversa naturaleza, permite detectar lugares revisitados en situaciones donde métodos basados solo en intensidad o en profundidad presentan dificultades (e.g. condiciones de iluminación deficientes, o falta de geometría). Además, se ha diseñado el método teniendo en cuenta su eficiencia, recurriendo para ello al detector FAST para extraer las características de las observaciones y al descriptor binario BRIEF. La detección de bucle se completa con una bolsa de palabras binarias. El rendimiento del método propuesto se ha evaluado en condiciones reales, obteniéndose resultados muy satisfactorios. Este trabajo se ha desarrollado en el marco de los proyectos TEP2012-530 y DPI2014-55826-R, financiados por la Junta de Andalucía y el Ministerio de Ciencia e Innovación respectivamente, ambos contando con fondos del Fondo Europeo de Desarrollo Regional (FEDER) Junta de Andalucía; TEP2012-530 https://doi.org/10.17979/spudc.9788497498081

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.

Simulations and synthetic datasets have historically empower the research in different service robotics-related problems, being revamped nowadays with the utilization of rich virtual environments. However, with their use, special attention must be paid so the resulting algorithms are not biased by the synthetic data and can generalize to real world conditions. These aspects are usually compromised when the virtual environments are manually designed. This article presents Robot@VirtualHome, an ecosystem of virtual environments and tools that allows for the management of realistic virtual environments where robotic simulations can be performed. Here “realistic” means that those environments have been designed by mimicking the rooms’ layout and objects appearing in 30 real houses, hence not being influenced by the designer’s knowledge. The provided virtual environments are highly customizable (lighting conditions, textures, objects’ models, etc.), accommodate meta-information about the elements appearing therein (objects’ types, room categories and layouts, etc.), and support the inclusion of virtual service robots and sensors. To illustrate the possibilities of Robot@VirtualHome we show how it has been used to collect a synthetic dataset, and also exemplify how to exploit it to successfully face two service robotics-related problems: semantic mapping and appearance-based localization. This work has been supported by the research projects WISER (DPI2017-84827-R), funded by the Spanish Government and financed by the European Regional Development’s funds (FEDER), ARPEGGIO (PID2020-117057GB-I00), funded by the European H2020 program, by the grant number FPU17/04512 and the UG PHD scholarship pro-gram from the University of Groningen. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal used for this research. We would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster

Jupyter notebooks are recently emerging as a valuable pedagogical resource in academy, being adopted in educational institutions worldwide. This is mainly due to their ability to combine the expressiveness of traditional explanations from textbooks, with the interaction capabilities of software applications, which provides numerous benefits for both students and lecturers of different fields. One of the areas that could benefit from their adoption is such of mobile robotics, whose recent popularity has resulted in an increasing demand of trained practitioners with a solid theoretical and practical background. Therefore, there is a need of high quality learning materials adapted to modern tools and methodologies. With that in mind, this work explores how the introduction of Jupyter notebooks in undergraduate mobile robotic courses contributes to improve both teaching and learning experiences. For that, we first present a series of (publicly available) educational notebooks encompassing a variety of topics relevant for robotics, with a particular emphasis in the study of mobile robots and commonly used sensors. Those documents have been built from the ground up to take advantage of the Jupyter Notebook framework, bridging the typical gap between theoretical frame and interactive code. We also present a case study describing the notebooks usage in undergraduate courses at University of M&aacute, laga, including a discussion on the promising results and findings obtained.