Curb Detection and Compensation Method for Autonomous Driving via a 3-D-LiDAR Sensor

Abstract

Effective detection of curbs is crucial for the perception and localization of autonomous vehicles, but, in practical applications, complex road scenes and the occlusion of curbs by various obstacles are two difficult problems to deal with. Therefore, this article proposes a fast curb detection and compensation method. First, the multiblock RANSAC method is used for ground segmentation to distinguish between ground points and nonground points. Second, the candidate curb points are obtained by processing the ground points with the jumping-window algorithm associated with multispatial features. The jumping-window algorithm only processes the region of interest, which greatly improves the operation efficiency of the method. Third, a joint filtering method is used to refine the candidate curb points, mainly to remove obstacles and other interfering points contained in the candidate curb points. Finally, the missing part of the curb caused by the obstruction is interpolated by a piecewise cubic spline to compensate for the curb points and improve the continuity. We use the KITTI dataset to evaluate the proposed method, and the obtained precision, recall, and harmonic mean in various road scenarios are all above 80%, which is sufficient to demonstrate the robustness of our method. At the same time, the average processing time of each frame is only 47 ms, which can meet the real-time requirements of automatic driving.