Stereo vision and LIDAR based Dynamic Occupancy Grid mapping : Application to scenes analysis for Intelligent Vehicles
type: Dataset
contact point: Organization
  fn: CCSD
keyword: classifiers
keyword: v-disparity
keyword: classifieurs
keyword: detection-et-reconnaissance-dobjets-en-mouvement
keyword: cameras-and-lidar-extrinsic-calibration
keyword: v-disparite
keyword: primitives-spatiales
keyword: kpca
keyword: moving-objects-detection-and-recognition
keyword: kernel-pca
keyword: odometrie-visuelle
keyword: calibrage-extrinseque-entre-cameras-et-lidar
keyword: spatial-features
keyword: u-disparity
keyword: theses
keyword: vehicule-intelligent
keyword: grille-doccupation-dynamique
keyword: dynamic-occupancy-grid-mapping
keyword: infoeu-reposemanticsdoctoralthesis
keyword: intelligent-vehicle
keyword: u-disparite
keyword: visual-odometry
keyword: infoinfo-cycomputer-science-cscomputers-and-society-cscy
landing page: https://theses.hal.science/tel-00982325
description: 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 …
identifier: NNT: 2013BELF0225
issued: 2026-05-05T13:32:57.796786
language: en
modified: 2026-05-05T13:32:57.796792

Stereo vision and LIDAR based Dynamic Occupancy Grid mapping : Application to scenes analysis for Intelligent Vehicles
type: Distribution
access URL: https://theses.hal.science/tel-00982325
format: HTML
issued: 2026-05-05T13:32:57.818763
modified: 2026-05-05T13:32:57.766665

test_moissonnage_selune
type: Agent