A simple ground segmentation method for LiDAR 3D point clouds

With benefits from wide-range detection and accurate measurements, LiDAR has been widely studied in the field of autonomous driving. Yet many challenges remain in modern LiDAR 3D point clouds processing algorithms. One is ground segmentation due to the real-time requirement dealing with huge input data from LiDAR. In this study, we propose a method to separate ground points from a point cloud in a hybrid way by adopting dynamic section division, height-based conditional filter, and multi-lines linear regression. Where, physical characteristics of LiDAR mounted on a vehicle have been introduced in the dynamic section division. Thereafter, we raise a conditional filter algorithm for filtering outliers of point clouds, and use multi-lines linear regression to generate the ground skeleton. In the end, the qualitative and quantitative experiments validate the performance by using two datasets and indicate that our proposed method outperforms state-of-the-art methods on KITTI in terms of accuracy 94.1% and runtime 432ms. The source code is publicly available under https://github.com/0-0cj/sgs.