A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles

In this paper, a slope-robust cascaded ground segmentation in 3D point cloud for autonomous vehicles is presented. In many challenging terrains encountered by autonomous vehicles where the ground does not have a simple planar shape such as sloped roads, many existing ground segmentation algorithms fail. The proposed algorithm aims to correctly segment ground points in scans where these challenging terrains are present. The proposed method consists of two main steps. First, filtering the majority of non-ground points using the geometry of the sensor and the distance between consecutive rings in the scan. In the second step, multi-region RANSAC plane fitting is used to separate remaining non-ground points from ground points in the scan. The 3D data was taken and partially labeled for quantitative evaluation. The experimental results were outstanding as the proposed algorithm could segment the ground correctly in various challenging terrains. The proposed algorithm could correctly segment ground points in the scan even in sloped terrains and achieved higher accuracy than other algorithms used in the evaluation.