An efficient curb detection and tracking method for intelligent vehicles via a high-resolution 3D-LiDAR

In this paper, we propose an efficient curb detection and trackingmethod (ECDT) based on high-resolution 3D-LiDAR.The ECDT follows manual-feature based technique routine, and the algorithm is consist by five steps. Firstly, the ECDT downsamples the raw point cloud and performs rough ground segmentation. Then we project the non-ground points into a density image under bird’s eye-view (BEV) and combine the improved beam model to realize the identification of road intersection and the classification of laser points. Thirdly, we down-sample the ground points and project them into BEV to obtain height difference image, and use gamma stretching upon the obtained image to extract candidate points. In addition, three filtering operation are applied to obtain accurate feature points. Finally, the algorithm perform curb fitting and tracking. In the experiment, we first compared the ECDT algorithm with the classic 3D curb algorithm, and verified the real-time performance of our method. Then we further test the ECDT under multiple complex scenarios by RSReference ground truth system. The results show that the proposed method has high accuracy and satisfactory robustness.