Abstract:

To address the problem of false background segmentation on 3D Lidar point clouds in the perception module of autonomous vehicles， an adaptive threshold segmentation method based on fluctuation range of road surface is proposed. At first， the original point cloud is divided into grids and the corresponding height threshold segmentation algorithm and the ground plane model segmentation algorithm are designed according to the number of points in a certain grid cell. Specifically， the ground plane model is fitted to a local point cloud subset inside the grid cell. Afterwards， an equation of road surface fluctuation is constructed for the problem of false segmentation in ground point cloud segmentation. Based on these point set distribution characteristics， an adaptive threshold method is used to realize an initial segmentation. Finally， the segmented point cloud subset is used to optimize the ground plane model and the segmentation threshold in an iterative manner. The paper proposes a unified benchmark dataset Semantic?Nova based on the open dataset Semantic?KITTI and performance evaluation indicators. Meanwhile， the performance test is conducted based on the actual scenes collected by the self?developed autopilot vehicle platform. The test results show that the adaptive threshold ground segmentation algorithm proposed in this paper can achieve high accuracy in benchmark dataset. Furthermore， it can meet the requirements of robustness and real?time applications in actual scenes， which has high engineering application value.

Key words: 3D laser point cloud, adaptive segmentation threshold, ground segmentation, plane model