LiDAR Road Edge Detection Algorithm Based on Target Detection
Road edge detection is an important part of the environment perception of autonomous vehicles.Effectively extraction road edge information from point cloud data is beneficial for target detection and drivable area detection.A solution was proposed to address the issue of point cloud road edge detection,taking into account the interference of road participants such as vehicles in road edge detection.Firstly,a ground point cloud segmentation algorithm was used to divide the original point cloud into ground and non-ground points.Sec-ondly,based on the intrinsic characteristics of vehicles and other road participants,a point cloud clustering algorithm was employed to cluster the points and filter out cloud non-ground points that meet the characteristics of vehicles and road participants.Thirdly,consider-ing that road edge point clouds can effectively occlude the line connecting the laser emission center point with non-road edge points in the two-dimensional plane,the road edge point cloud was extracted.Finally,the random sample consensus(RANSAC)algorithm was used to perform polynomial fitting on the road edge point cloud,and an extended Kalman filter was utilized to track the road edge.Ex-perimental results demonstrate that the proposed point cloud road edge detection algorithm can eliminate the interference of vehicles and other road participants on road edge detection,while satisfying real-time and robustness requirements for practical vehicle applications.
万方数据
维普
