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

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 estab...

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Published inComputational intelligence and neuroscience Vol. 2021; no. 1; p. 9927982
Main Authors Wang, Jingli, Zhang, Huiyuan, Gao, Jingxiang, Xiao, Dong
Format Journal Article
LanguageEnglish
Published New York Hindawi 2021
John Wiley & Sons, Inc
Subjects
Online AccessGet full text
ISSN1687-5265
1687-5273
1687-5273
DOI10.1155/2021/9927982

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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.
AbstractList 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.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.
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.
Audience Academic
Author Zhang, Huiyuan
Xiao, Dong
Gao, Jingxiang
Wang, Jingli
AuthorAffiliation 1 School of Transportation Engineering, Shenyang Jianzhu University, Shenyang, China
3 Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang 110819, China
4 School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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CitedBy_id crossref_primary_10_3788_CJL221153
crossref_primary_10_3389_fphy_2022_1083558
crossref_primary_10_3390_s23167208
crossref_primary_10_1088_1361_6501_ad4e55
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ContentType Journal Article
Copyright Copyright © 2021 Jingli Wang et al.
COPYRIGHT 2021 John Wiley & Sons, Inc.
Copyright © 2021 Jingli Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
Copyright © 2021 Jingli Wang et al. 2021
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– notice: Copyright © 2021 Jingli Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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  ident: e_1_2_9_20_2
  article-title: Linear regression modeling and solution method based on total least squares
  publication-title: Journal of Wuhan University (Natural Science Edition)
– ident: e_1_2_9_4_2
  doi: 10.1109/ICCV.2017.253
– ident: e_1_2_9_11_2
  doi: 10.1145/1276377.1276405
– ident: e_1_2_9_3_2
  doi: 10.1109/TSP.2017.2771730
– ident: e_1_2_9_5_2
  doi: 10.1111/cgf.12139
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Snippet With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very...
With the further development of the construction of “smart mine,” the establishment of three‐dimensional (3D) point cloud models of mines has become very...
With the further development of the construction of "smart mine," the establishment of three-dimensional (3D) point cloud models of mines has become very...
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StartPage 9927982
SubjectTerms 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
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Title Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM
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