3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR

Abstract

Light Detection and Ranging (LiDAR) is widely used in the perception of physical environment to complete object detection and tracking tasks. The current methods and datasets are mainly developed for autonomous vehicles, which could not be directly used for roadside perception. This paper presents a 3D point cloud stitching method for object detection with wide horizontal field of view (FoV) using roadside LiDAR. Firstly, the base detection model is trained by KITTI dataset and has achieved detection accuracy of 88.94. Then, a new detection range of 180° can be inferred to break the limitation of camera’s FoV. Finally, multiple sets of detection results from a single LiDAR are stitched to build a 360° detection range and solve the problem of overlapping objects. The effectiveness of the proposed approach has been evaluated using KITTI dataset and collected point clouds. The experimental results show that the point cloud stitching method offers a cost-effective solution to achieve a larger FoV, and the number of output objects has increased by 77.15% more than the base model, which improves the detection performance of roadside LiDAR.