Abstract
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.
Highlights
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
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. e 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 (GATELM). e steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-two-hidden-layer extreme learning machine (TELM) to repair holes. e advantages of the proposed method are listed as follows
Summary
Received 24 March 2021; Revised 22 August 2021; Accepted 24 August 2021; Published 14 September 2021. 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 (GATELM). Oztireli et al [10] proposed a new point-based surface definition method to solved some problems of performing approximation in the sense of least squares, which can handle sparse samples, can retain the fine features of the samples, and shows the superiority to a certain extent. Erefore, this paper proposes the point cloud denoising method combining orthogonal total least squares fitting and two-layer extreme learning machine improved by the genetic algorithm (GA-TELM). For the hollow model whose point cloud is only concentrated on the surface of the object (Figure 1), the above method applied to the hollow model would cause holes in the model. erefore, this paper proposes the point cloud denoising method combining orthogonal total least squares fitting and two-layer extreme learning machine improved by the genetic algorithm (GA-TELM). e main steps are to separate the dust points and the ground points with the orthogonal total least squares fitting method and use GA-TELM to repair the holes. e results show that this method can remove noise points without producing holes. e innovations of the paper include the following three points: (1) this method can denoise without generating holes, which solves engineering problems and has engineering significance; (2) compared with other algorithms, GA-TELM has a better effect on repairing holes; and (3) this method has a better treatment effect for other situations with similar problems
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