The point cloud data scanned by the low-beam LiDAR is relatively sparse, resulting in poor 3D object detection in the unmanned environment perception system. Multi-frame point cloud registration can achieve sparse point cloud densification, however, pedestrians and moving vehicles in a dynamic environment will reduce the positioning accuracy of the LiDAR, and will also cause a large offset of the point cloud on the moving object in the fusion frame. Aiming at the above problems, this paper proposed a multi-frame point cloud fusion algorithm in a dynamic environment, and used this algorithm to detect 3D objects in the real situation of park roads, which improved the 3D object detection accuracy of low-beam LiDAR. This paper used the driving road condition data collected by 16-line and 40-line LiDAR to conduct experiments. The results show that the algorithm can enhance the density of sparse point clouds and improve the environmental perception ability of low-cost LiDAR.

[2022-09-23] Accepted Paper /doc/2022.05.0345/v1

[2023-03-05] Printed Article /doc/2022.05.0345/v2