A Road Segmentation Method Based on Reorganized LiDAR Points and Line Scanning

Road segmentation is an important part of autonomous driving vehicles. Reliable road segmentation results are a prerequisite for autonomous driving tasks, e.g., path planning In this paper, we propose a road segmentation method based on 3D point cloud organization and line scanning of LiDAR data. Our model assumes that road areas are always flatter than non-road areas. First, we propose an adjacent-line-difference (ALD) feature to define the flatness of the point cloud. Using this feature, the approximate road area can be estimated. Then, we proposed a horizontal and vertical scanning strategy to obtain a more accurate road area. In order to prove the effectiveness of our method, experiments are conducted on the public KITTI-Road benchmark. The experimental results prove that the proposed approach can achieve one of the best performances among all LiDAR-based methods.