Study on coal mine tunnels surface displacement monitoring based on 3D laser point cloud comparison

Abstract

Monitoring the surface displacement of coal mine tunnel is very important to ensure the safe and stable operation of the mine. By combining the downsampling algorithm based on the normal vector of point cloud with the multiscale model-to-model point cloud comparison algorithm, the feature extraction and integrity monitoring of tunnel surface can be realized. The three-dimensional laser point cloud obtained by the three-dimensional laser scanner is used to reconstruct the roadway surface, and the three-dimensional point cloud model is obtained. The point cloud comparison algorithm is used to process the two periods of point cloud data in different periods. The color value is used to represent the regional change of coal mine tunnel displacement, which is applied to the actual tunnel surface displacement monitoring. The research shows that the down-sampling algorithm based on point cloud normal vector and M3C2 algorithm are combined to successfully extract the feature area and realize the overall monitoring of tunnel surface displacement. This method can directly reflect the deformation area of tunnel surface, and its performance is better than that of directly applying M3C2 algorithm in tunnel.

Keywords

• measurement of coal mine roadway
• surface displacement monitoring
• three-dimensional laser point cloud
• m3c2 algorithm
• point cloud normal vector