High-Precision Motion Detection and Tracking Based on Point Cloud Registration and Radius Search

Detecting and tracking dynamic objects in a scene using point cloud data collected by LiDAR and estimating the motion state of objects with high accuracy are challenges for autonomous driving technology. In this study, a motion detection method based on point cloud registration is investigated to detect motion through the overlapping relationship between source and target point clouds after registration and extract moving objects using clustering and scale analysis by combining the object information of interest acquired by deep learning networks. Next, object association is achieved by object motion information and geometric and texture features. Then, a point cloud registration method flow is designed to estimate the motion state of the object with high accuracy by point cloud registration. The detection, tracking and estimation of the accurate motion state of moving objects are achieved.