Curb detection and tracking using 3D-LIDAR scanner

Abstract

This paper presents a novel road curb detection method using 3D-LIDAR scanner. To detect the curbs, the ground points are separated from the pointcloud first. Then the candidate curb points are selected using three spatial cues: the elevation difference, gradient value and normal orientation. Afterwards the false curb points caused by obstacles are removed using the short-term memory technique. Next the curbs are fitted using the parabola model. Finally, the particle filter is used to smooth the curb detection result. The proposed approach was evaluated on a dataset collected by an autonomous ground vehicle driving around the Ford Research campus and downtown Dearborn. Our curb detection results are accurate and robust despite variations introduced by moving vehicles and pedestrians, static obstacles, road curvature changes, etc.