Stereo vision and LIDAR based Dynamic Occupancy Grid mapping : Application to scenes analysis for Intelligent Vehicles

• Main Author: Li, You
• Language: English
• Published: Université de Technologie de Belfort-Montbeliard 2013
• Subjects: [INFO:INFO_CY] Computer Science/Computers and Society; [INFO:INFO_CY] Informatique/Ordinateur et société; Intelligent vehicle; Visual odometry; Moving objects detection and recognition; Cameras and lidar Extrinsic calibration; Dynamic occupancy grid mapping; U-disparity; V-disparity; Spatial features; Kernel-PCA; Classifiers
• Online Access: http://tel.archives-ouvertes.fr/tel-00982325; http://tel.archives-ouvertes.fr/docs/00/98/23/25/PDF/These_LI_-_You_UTBM.pdf

Intelligent vehicles require perception systems with high performances. Usually, perception system consists of multiple sensors, such as cameras, 2D/3D lidars or radars. The works presented in this Ph.D thesis concern several topics on cameras and lidar based perception for understanding dynamic scenes in urban environments. The works are composed of four parts.In the first part, a stereo vision based visual odometry is proposed by comparing several different approaches of image feature detection and feature points association. After a comprehensive comparison, a suitable feature detector and a feature points association approach is selected to achieve better performance of stereo visual odometry. In the second part, independent moving objects are detected and segmented by the results of visual odometry and U-disparity image. Then, spatial features are extracted by a kernel-PCA method and classifiers are trained based on these spatial features to recognize different types of common moving objects e.g. pedestrians, vehicles and cyclists. In the third part, an extrinsic calibration method between a 2D lidar and a stereoscopic system is proposed. This method solves the problem of extrinsic calibration by placing a common calibration chessboard in front of the stereoscopic system and 2D lidar, and by considering the geometric relationship between the cameras of the stereoscopic system. This calibration method integrates also sensor noise models and Mahalanobis distance optimization for more robustness. At last, dynamic occupancy grid mapping is proposed by 3D reconstruction of the environment, obtained from stereovision and Lidar data separately and then conjointly. An improved occupancy grid map is obtained by estimating the pitch angle between ground plane and the stereoscopic system. The moving object detection and recognition results (from the first and second parts) are incorporated into the occupancy grid map to augment the semantic meanings. All the proposed and developed methods are tested and evaluated with simulation and real data acquired by the experimental platform "intelligent vehicle SetCar" of IRTES-SET laboratory.