Abstract
It is fundamental to acquire accurate point cloud information on rock discontinuities efficiently and comprehensively when evaluating the stability of rock masses. Taking a high and steep cliff as an example, we combined 3D laser scanning and UAV photogrammetry technology to collect rock data, and proposed an intelligent identification method for rock discontinuities based on the multi-source fusion of point clouds. First, the 3D-laser-collected point cloud data were used as the basis to fuse with the UAV-photogrammetry-collected data, and the unified coordinate system and improved ICP algorithm were used to obtain the complete 3D point cloud in the study area. Secondly, we used neighborhood information entropy to achieve adaptive neighborhood-scale selection and to obtain the optimal neighborhood radius for the KNN search, to effectively calculate the point cloud normal vector and rock mass orientation information. Finally, the KDE algorithm and DBSCAN algorithm were combined for rock discontinuity clustering to achieve intelligent identification and information extraction of the rock structural plane. The clustering results were imported into the DSE program developed based on Matlab to calculate the discontinuity spacing and continuity of the rock mass structure, and to efficiently obtain the parameters of rock mass occurrence. The research results showed that this method can effectively solve the problem of incomplete-data-acquisition ground 3D laser scanning in complex geological conditions, and UAV photogrammetry prone to blurred images in depressed areas. When the extraction results were compared with the field-measured rock occurrence, the average dip angle error was about 2°, the average dip direction error was 1°, and the recognition results met the accuracy requirements. The research results provide a feasible scheme for the identification and extraction of discontinuities of high and steep rock masses.
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