Intelligent detection technology of roughness in mining tunnel based on 3D laser scanning

To improve the level of roughness detection in the construction process of mining tunnels, scholars domestic and abroad have combined 3D laser scanning with other information technology to achieve certain research results. However, the existing research mainly calculates roughness by the distance from the maximum concave and convex points to the fitting plane, which does not meet the requirements of the current regulations for the rule method. To address these issues, this paper uses algorithms such as cutting, denoising, complementation, and downsampling for pre-processing of laser scanning point clouds. Using the K-D tree and Octree algorithms, the rule method prescribed in the regulations is simulated, and 3D roughness is calculated. Through application verification of actual engineering projects, automation and intelligentization of roughness detection during the construction phase of mining tunnels are achieved.