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Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems

Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete...

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Autores principales: Chan, Ting On, Xia, Linyuan, Lichti, Derek D., Sun, Yeran, Wang, Jun, Jiang, Tao, Li, Qianxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471979/
https://www.ncbi.nlm.nih.gov/pubmed/32824328
http://dx.doi.org/10.3390/s20164594
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author Chan, Ting On
Xia, Linyuan
Lichti, Derek D.
Sun, Yeran
Wang, Jun
Jiang, Tao
Li, Qianxia
author_facet Chan, Ting On
Xia, Linyuan
Lichti, Derek D.
Sun, Yeran
Wang, Jun
Jiang, Tao
Li, Qianxia
author_sort Chan, Ting On
collection PubMed
description Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints’ geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.
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spelling pubmed-74719792020-09-17 Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems Chan, Ting On Xia, Linyuan Lichti, Derek D. Sun, Yeran Wang, Jun Jiang, Tao Li, Qianxia Sensors (Basel) Article Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints’ geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively. MDPI 2020-08-16 /pmc/articles/PMC7471979/ /pubmed/32824328 http://dx.doi.org/10.3390/s20164594 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chan, Ting On
Xia, Linyuan
Lichti, Derek D.
Sun, Yeran
Wang, Jun
Jiang, Tao
Li, Qianxia
Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
title Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
title_full Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
title_fullStr Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
title_full_unstemmed Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
title_short Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
title_sort geometric modelling for 3d point clouds of elbow joints in piping systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471979/
https://www.ncbi.nlm.nih.gov/pubmed/32824328
http://dx.doi.org/10.3390/s20164594
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