Spherical Coordinates Based Methods of Ground Extraction and Objects Segmentation Using 3-D LiDAR Sensor

This paper proposes new methods to extract ground points and segment non-ground objects based on the spherical coordinates of point clouds collected by the 3-D LiDAR sensor system. Ground points extraction and non-ground objects segmentation are two primary issues in environment perception of autonomous driving. Firstly, the new method to extract ground points is described in this paper. It is based on the breakpoints and turning points of the radial distance curve in the spherical coordinates without assuming the road is located in the lowest. Then the algorithm for non-ground objects segmentation is proposed, which uses azimuth angle and radial distance in the spherical coordinates as judgment criterion. This algorithm works efficiently and it can avoid over-segmentation and under-segmentation. Finally, the experimental results using the point clouds acquired by the sensor Velodyne HDL-32E are presented. Comparing to the existing methods, the results show the advantages of methods based on the spherical coordinates.