Abstract
A ground segmentation method based on line fitting of adjacent points was proposed for accurate and real-time segmentation of non-ground information from the LiDAR point cloud. Firstly, the point cloud is divided into several ordered regions depending upon the distribution characteristics of the LiDAR’s concentric circles. Then, the Euclidean distance between adjacent points and the spatial geometric features of ground point clouds is used for adaptive line fitting of ground point clouds. Finally, the ground points are divided by the distance between the adjacent points and the outer points of the line. The experiment was conducted using a real car and the KITTI open-source dataset. The approach presented in this research substantially enhances the accuracy of ground segmentation while ensuring real-time performance.
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