Automatic Recognition and Feature Extraction of Rock Blocks Based on 3D Point Cloud Data Analytics

Abstract

Rock mass fraction is one of the main indexes to evaluate the blasting effect of mining. We take some rock blocks after blasting as the research objects and use 3D laser scanner to obtain the point cloud data of rock blocks. Then we use statistical filtering method to process the original point cloud data, and then calculate the point cloud data after pre-processing. We obtain the supervoxel clustering point cloud. On the supervoxel clustering algorithm, the concave convex criterion is used to fuse the clustering results. The regional growth algorithm is used to complete the segmentation of rock point cloud, so as to achieve the purpose of automatic recognition of blasting rock block contour. Based on the segmentation results of the rock block point cloud, the rock block point cloud with obvious characteristics is extracted, and the length of the long axis of the rock block is obtained according to the feature information of the rock block. The results show that the method can solve the defects of traditional measurement methods. The proposed recognition algorithm will meet the requirement of the intelligent of blasting fragmentation analysis. Additionally, it will satisfy the requirements of blasting quality analysis and evaluation.