Campus unmanned logistics vehicles can effectively reduce the congestion of express stores and improve delivery efficiency.In view of the disorderly passage of people in the campus,the road markings are not obvious,etc.,road environment information was collected useing the Velodyne VLP-16 Lidar.The echo signal was decomposed by the method of variational mode decomposition(VMD),and then the correlation between the modes was distinguished by the Bhattacharyian distance.The non-correlation mode was processed by moving average method,and then the correlation mode signal and the processed non-correlation mode signal were reconstructed to improve the signal-to-noise ratio of the signal compared with other filtering methods.The RANSAC algorithm was used to segment the point cloud ground,and then the European clustering algorithm was used to cluster the point cloud of campus road obstacles to realize the detection of obstacles.
NETL
NSTL
万方数据
维普
