A Point Clouds Segmentation Method for Tunnel Face Based on Spatial Gradient Filtering and k-means Clustering
Aiming at the problem that the existing surface filtering methods might partially apply to the point clouds segmentation of the tunnel face of mineral tunnels,a segmentation method for the tunnel face based on spatial gradient filtering and k-means clustering was proposed.The process removed the connection between the tunnel face and the tunnel wall based on a spatial gradient filtering,and the k-means clustering was used to separate different point cloud groups and retain the point clouds segmentation results of the tunnel face.Finally,the A ABB bounding box was used to screen the filtered out point clouds to fill in the interior holes of the tunnel face caused by over-segmentation.Four groups of real mine lidar point cloud sets were selected in the test,and the robustness of the proposed algorithm was verified from different dimensions such as tunnel morphology,point cloud density and attitude angle deviation.The test results show that under the optimal hyper-parameter conditions,the accuracy,recall and comprehensive evaluation index F1 of the algorithm reach 97.80％,98.98％and 98.38％,respectively,which are better than the selected comparison algorithm.The comparative experiments show that the algorithm can realize the point clouds segmentation of the tunnel face under the condition of complex surface topography,and has the characteristics of high precision,good maneuverability and high robustness.
Lidar point cloudsTunnel faceTunnel wallSpatial gradient filteringEuropean clustering
NETL
NSTL
万方数据
