A 3D Omnidirectional Sensor For Mobile Robot Applications

International audienc

CUDA-based SeqSLAM for real-time place recognition

Vehicle localization is a fundamental issue in autonomous navigation that has been extensively studied by the Robotics community. An important paradigm for vehicle localization is based on visual place recognition which relies on learning a database, then consecutively trying to find matchings between this database and the actual visual input. An increasing interest has been directed to visual place recognition in varying conditions like day and night cycles and seasonal changes. A major approach dealing with such challenges is Sequence SLAM (SeqSLAM) based on matching a sequence of images to the database instead of a single image. This algorithm allows global pose recovery at the expense of a higher computational time. To solve this problem with a certain amount of speedup, we propose in this work, a CUDA-based solution for real-time place recognition with SeqSLAM. We design a mapping of SeqSLAM to CUDA architecture and we describe, in detail, our hardware-specific implementation considerations as well as the parallelization methods. Performance analysis against existing CPU implementation is also given, showing a speedup to six times faster than the CPU for common sized databases. More speedup could be obtained when dealing with bigger databases

Vision based vehicle relocalization in 3D line-feature map using Perspective-n-Line with a known vertical direction

Common approaches for vehicle localization propose to match LiDAR data or 2D features from cameras to a prior 3D LiDAR map. Yet, these methods require both heavy computational power often provided by GPU, and a first rough localization estimate via GNSS to be performed online. Moreover, storing and accessing 3D dense LiDAR maps can be challenging in case of city-wide coverage. In this paper, we address the problem of camera global relocalization in a prior 3D line-feature map from a single image, in a GNSS denied context and with no prior pose estimation. We propose a dual contribution. (1) We introduce a novel pose estimation method from lines, (i.e. Perspective-n-Line or PnL), with a known vertical direction. Our method benefits a Gauss-Newton optimization scheme to compensate the sensor-induced vertical direction errors, and refine the overall pose. Our algorithm requires at least 3 lines to output a pose (P3L) and requires no reformulation to operate with a higher number of lines. (2) We propose a RANSAC (RANdom SAmple Consensus) 2D-3D line matching and outliers removal algorithm requiring solely one 2D-3D line pair to operate, i.e. RANSAC1. Our method reduces the number of iteration required to match features and can be easily modified to exhaustively test all feature combinations. We evaluate the robustness of our algorithms with a synthetic data, and on a challenging sub-sequence of the KITTI dataset

Multi-Agent Cooperative Camera-Based Evidential Occupancy Grid Generation

International audienceAbout a decade ago the idea of cooperation has been introduced to self-driving with the aim to enhance safety in dangerous places such as intersections. Infrastructure-based cooperative systems emerged very recently bringing a new point of view of the scene and more computation power. In this paper, we want to go beyond the framework presented in the vehicle-to-infrastructure (V2I) cooperation by including the vehicle's point of view in the perception of the environment. To keep the cost low, we decided to use only two-dimensional bounding boxes, thus depriving ourselves of depth information that contrasts with state-of-the-art methods. With this in-thescene point-of-view, we propose a new framework to generate a cooperative evidential occupancy grid based on the Dempster-Shafer Theory and which employs a Monte Carlo framework to incorporate position noise in our algorithm. We also provide a new cooperative dataset generator based on the CARLA simulator. Finally, we provide an extended review of our new cooperative occupancy grid map generation method which improves the state-of-the-art techniques