Fault identification method of transmission corridor based on 3D R-tree integrated point cloud data segmentation

Abstract

This paper addresses the inefficient and error-prone identification of tree hazards near transmission corridors by proposing a recognition method that uses 3D R-tree-integrated point cloud segmentation. LiDAR-equipped UAVs capture the original point cloud data of the corridor. The data are denoised and enhanced using principal component analysis (PCA). A 3D R-tree-integrated octree indexing structure is constructed to rapidly locate potential tree hazards by querying regions within the minimum safe distance. A Euclidean clustering algorithm with cylinder k-point constraints is applied to extract these hazardous point clouds. The spatial location of the transmission corridor is determined by fitting its point cloud via RANSAC-based least squares. Finally, distances between tree crowns and the corridor are calculated to identify hazards. Experiments demonstrate that the proposed method is efficient, accurate, and sensitive, offering an intelligent solution for automated corridor monitoring.