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The camera is one of the important sensors to realise the intelligent driving environment. It can realise lane detection and tracking, obstacle detection, traffic sign detection, identification and discrimination and visual simultaneous localisation and mapping. The visual sensor model, quantity and installation location are different on different intelligent driving hardware experimental platform as well as the visual sensor information processing module, thus a number of intelligent driving system software modules and interfaces are different. In this study, the software architecture of the autonomous vehicle based on the driving brain is used to adapt to different types of visual sensors. The target segment is extracted by the image segmentation algorithm, and then the segmentation of the region of interest is carried out. According to the input feature calculation results, the obstacle search is done in the second segmentation region, the output of the accessible road area. As driving information is complete, the authors will increase or reduce one or more visual sensors, change the visual sensor model or installation location, which will no longer directly affect the intelligent driving decision, they make the multi-vision sensors adapted to the requirements of different intelligent driving hardware test platforms.

Indoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot., This work was supported by the MISTI-Spain for the financial support in the project entitled Drone Autonomy and the Spanish Ministry of Economy and Competitivity for its funding Project (Complex Coordinated Inspection and Security missions by UAVs in cooperation with UGV) under Grant RTI2018-100847-B-C21. The work of Paloma de la Puente was supported in part by the Spanish Ministry of Economics and Competitivity under Grant DPI2017-86915-C3-3-R COGDRIVE.

This paper addresses the problem of simultaneous estimation of a vehicle's ego motion and motions of multiple moving objects in the scene–called eoru motions –through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix . First, the ego motion is segmented and then several hypotheses are generated for the eoru motions . All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset ( Hopkins 155 ) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community.

Diminished reality aims to remove real objects from video images and fill in the missing regions with plausible background textures in real time. Most conventional methods based on image inpainting achieve diminished reality by assuming that the background around a target object is almost planar. This paper proposes a new diminished reality method that considers background geometries with less constraints than the conventional ones. In this study, we approximate the background geometry by combining local planes, and improve the quality of image inpainting by correcting the perspective distortion of texture and limiting the search area for finding similar textures as exemplars. The temporal coherence of texture is preserved using the geometries and camera pose estimated by visual-simultaneous localization and mapping (SLAM). The mask region that includes a target object is robustly set in each frame by projecting a 3D region, rather than tracking the object in 2D image space. The effectiveness of the proposed method is successfully demonstrated using several experimental environments.

There is several implementations of techniques for solving the SLAM problem that is a process to expand autonomous mobile robots field. In this context, we will be comparing swarm algorithms for solving the simultaneous localization and mapping problem such as Firefly Algorithm, Particle Swarm Optimization and Glowworm Swarm Optimization, we directed a group of experiments using a wireless system composed of 2D laser range finder UTM-30LX, iRobot Roomba 600 and Raspberry Pi 3 as an experimental environment., © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 21-23 Oct. 2018, Washington, DC, USA

In this paper, we analyze and extend the recently proposed closed-form online pose-chain simultaneous localization and mapping (SLAM) algorithm. Pose-chains are a specific type of extremely sparse pose-graphs and a product of contemporary SLAM front-ends, which perform accurate visual odometry and reliable appearance-based loop detection. They are relevant for challenging robotic applications in large-scale 3-D environments for which frequent loop detection is not desired or not possible. Closed-form online pose-chain SLAM efficiently and accurately optimizes pose-chains by exploiting their Lie group structure. The convergence and optimality properties of this solution are discussed in detail and are compared against state-of-the-art iterative methods. We also provide a novel solution space, that of similarity transforms, which has not been considered earlier for the proposed algorithm. This allows for closed-form optimization of pose-chains that exhibit scale drift, which is important to monocular SLAM systems. On the basis of extensive experiments, specifically targeting 3-D pose-chains and using a total of 60 km of challenging binocular and monocular data, it is shown that the accuracy obtained by closed-form online pose-chain SLAM is comparable with that of state-of-the-art iterative methods, while the time it needs to compute its solution is orders of magnitudes lower. This novel SLAM technique thereby is relevant to a broad range of robotic applications and computational platforms.

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicting the risk of a failed alignment is learned by analysing the degree of spatial overlap between two input point clouds and the geometric constraints available within the region of overlap. We define a novel measure of alignability for these constraints. The method is evaluated against three real-world datasets and compared to baseline approaches. The experiments demonstrate how our approach can help improve the reliability of laser-based localization during exploration of unknown and cluttered man-made environments.

Autonomous navigation of small Unmanned Aerial Vehicles (UAVs) in cluttered environments is still a challenging problem. In this work, we present an approach based on graph slam and loop closure detection for online mapping of unknown outdoor environments using a small UAV. Here, we used an onboard front facing stereo camera as the primary sensor. The data extracted by the cameras are used by the graph-based slam algorithm to estimate the position and create the graph-nodes and construct the map. To avoid multiple detections of one object as different objects and to identify re-visited locations, a loop closure detection is applied with optimization algorithm using the g2o toolbox to minimize the error. Furthermore, 3D occupancy map is used to represent the environment. This technique is used to save memory and computational time for the online processing. Real experiments are conducted in outdoor cluttered and open field environments. The experiment results show that our presented approach works under real time constraints, with an average time to process the nodes of the 3D map is 17.79ms.

This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment or remediation. An important aspect of the overall system design is to keep the size and cost of the reacquisition vehicle as low as possible, as it may potentially be destroyed in the reacquisition mission. This low-cost requirement prevents the use of sophisticated AUV navigation sensors, such as a Doppler velocity log (DVL) or an inertial navigation system (INS). Our system instead uses the Proviewer 900-kHz imaging sonar from Blueview Technologies, which produces forward-looking sonar (FLS) images at ranges up to 40 m at approximately 4 Hz. In large volumes, it is hoped that this sensor can be manufactured at low cost. Our approach uses a novel simultaneous localization and mapping (SLAM) algorithm that detects and tracks features in the FLS images to renavigate to a previously mapped target. This feature-based navigation (FBN) system incorporates a number of recent advances in pose graph optimization algorithms for SLAM. The system has undergone extensive field testing over a period of more than four years, demonstrating the potential for the use of this new approach for feature reacquisition. In this report, we review the methodologies and components of the FBN system, describe the system's technological features, review the performance of the system in a series of extensive in-water field tests, and highlight issues for future research. QC 20130822

This paper presents a method of occluding depth edge-detection targeted towards RGB-D video streams and explores the use of these and other edge features in RGB-D SLAM. The proposed depth edge-detection approach uses prior information obtained from the previous RGB-D video frame to determine which areas of the current depth image are likely to contain edges due to image similarity. By limiting the search for edges to these areas a significant amount of computation time is saved compared to searching the entire image. Pixels belonging to both the depth and colour edges of an RGB-D image can be back projected using the depth component to form 3D point clouds of edge points. Registration between such edge point clouds is achieved using ICP and we present a realtime RGB-D SLAM system utilizing such back projected edge features. Experimental results are presented demonstrating the performance of both the proposed depth edge-detection and SLAM system using publicly available datasets.

In this paper we propose a framework for trajectory planning and control of tracked vehicles for rescue environments, based on Augmented Reality (AR). The framework provides the human operator with an AR-based interface that facilitates both 3D path planning and obstacle negotiation. The interface converts the 3D movements of a marker pen, handheld by the operator, into trajectories feasible for the tracked vehicle. The framework implements a trajectory tracking controller to allow the tracked vehicle to autonomously follow the trajectories, decided by the operator. This controller relies on a localization system which provides, at real-time, position feedback. The localization system exploits the performance of a Dead Reckoning System together with the accuracy of an ICP-based SLAM in pose estimation, to determine the pose of the tracked vehicle within the 3D map. We demonstrate the application of the planning framework in autonomous robot navigation for evaluating the robot capabilities in rescue environments. Our experiments show the effectiveness of the trajectory tracking control method.

This article presents a real-time mosaicking algorithm based on a SLAM framework. The mosaic of the seafloor can be useful in real time for a ROV operator that is piloting the ROV. Two important aspects arise in this kind of work: data association and computational time. In order to solve the first one, a combination of SURF features and template correlation methods is used. To minimize the computational time, a very recent approach in the domain of feature description is used: BRIEF binary features. Finally, to be able to update the whole mosaicking in a fast and easy way, local mosaics are used instead of a global one. The algorithm was tested using data collected in a typical experiment and the results show the improvement with respect to previous versions of a similar algorithm. © 2013 IEEE.

This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the system's ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment., Lincoln Laboratory, United States. Air Force (Contract FA8721-05-C-0002), United States. Office of Naval Research (Grant N00014-10-1-0936), United States. Office of Naval Research (Grant N00014-11-1-0688), United States. Office of Naval Research (Grant N00014-12-10020)

This paper presents a new solution to the loop closing problem for 3D point clouds. Loop closing is the problem of detecting the return to a previously visited location, and constitutes an important part of the solution to the Simultaneous Localisation and Mapping (SLAM) problem. It is important to achieve a low level of false alarms, since closing a false loop can have disastrous effects in a SLAM algorithm. In this work, the point clouds are described using features, which efficiently reduces the dimension of the data by a factor of 300 or more. The machine learning algorithm AdaBoost is used to learn a classifier from the features. All features are invariant to rotation, resulting in a classifier that is invariant to rotation. The presented method does neither rely on the discretisation of 3D space, nor on the extraction of lines, corners or planes. The classifier is extensively evaluated on publicly available outdoor and indoor data, and is shown to be able to robustly and accurately determine whether a pair of point clouds is from the same location or not. Experiments show detection rates of 63% for outdoor and 53% for indoor data at a false alarm rate of 0%. Furthermore, the classifier is shown to generalise well when trained on outdoor data and tested on indoor data in a SLAM experiment. ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Karl Granström and Thomas Schön, Learning to Close the Loop from 3D Point Clouds, 2010, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2089-2095.

Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. The sensors have a large impact on the algorithm used for SLAM. In this work a novel method, called Filtered Inverse Depth Delayed (FIDD) Initialization which is intended for initializing new features in Bearing-Only SLAM systems. Unlike range sensors which provide range and angular information, a bearing sensor (e.g. cameras) measures only the bearing (angular information) of features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. The proposed approach is based in an inverse depth parameterization and delayed initialization scheme. The main idea is to incorporate to the SLAM process, an extra uncorrelated filter, which progressively incorporates the new bearing measurements needed to estimate the full state of each new feature. Several simulations are given in order to show the performance of the proposed approach.

Micro aerial vehicles (MAVs) are a rapidly growing area of research and development in robotics. For autonomous robot operations, localization has typically been calculated using GPS, external camera arrays, or onboard range or vision sensing. In cluttered indoor or outdoor environments, onboard sensing is the only viable option. In this paper we present an appearance-based approach to visual SLAM on a flying MAV using only low quality vision. Our approach consists of a visual place recognition algorithm that operates on 1000 pixel images, a lightweight visual odometry algorithm, and a visual expectation algorithm that improves the recall of place sequences and the precision with which they are recalled as the robot flies along a similar path. Using data gathered from outdoor datasets, we show that the system is able to perform visual recognition with low quality, intermittent visual sensory data. By combining the visual algorithms with the RatSLAM system, we also demonstrate how the algorithms enable successful SLAM.