Abstract: This paper proposes a vehicle target tracking and localization algorithm using roadside LiDAR based on Kalman filter. Firstly, the position calibration of roadside LiDAR is performed based on reflective strips; the background point cloud map is constructed, and the background differential filtering is performed on the point cloud data collected by roadside LiDAR, and only the foreground point cloud data such as motor vehicles, non-motor vehicles and pedestrians are retained; the density-based DBSCAN clustering is performed on the foreground point cloud, and the point cloud is divided into point cloud clusters; then the SVM classifier is trained, and the extracted point cloud cluster features are used for SVM-based target classification recognition; finally, the inter-frame association of targets is realized by the nearest neighbor algorithm, and the trajectories of target objects are predicted and updated by linear Kalman filtering. In the part of accuracy evaluation, the calibration results and the algorithm positioning results are analyzed for accuracy using relative position error and absolute trajectory error. From the analysis results, it can be seen that the optimized smooth trajectory obtained by the trajectory estimation method with Kalman filter is more consistent with the actual operation of the vehicle and can significantly improve the target tracking accuracy.

Key words: roadside LiDAR, object recognition, object tracking, Kalman filter, point cloud filtering