Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 9927982
• Main Authors: Wang, Jingli, Zhang, Huiyuan, Gao, Jingxiang, Xiao, Dong
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Artificial neural networks; Data models; Dust; Dust control; Engineering; Genetic algorithms; Learning algorithms; Least squares; Machine learning; Methods; Noise; Noise reduction; Principal components analysis; Software; Three dimensional models; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/9927982

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.