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Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud
Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquire...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730831/ https://www.ncbi.nlm.nih.gov/pubmed/33260677 http://dx.doi.org/10.3390/s20236815 |
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author | Yi, Cheng Lu, Dening Xie, Qian Xu, Jinxuan Wang, Jun |
author_facet | Yi, Cheng Lu, Dening Xie, Qian Xu, Jinxuan Wang, Jun |
author_sort | Yi, Cheng |
collection | PubMed |
description | Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method. |
format | Online Article Text |
id | pubmed-7730831 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77308312020-12-12 Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud Yi, Cheng Lu, Dening Xie, Qian Xu, Jinxuan Wang, Jun Sensors (Basel) Article Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method. MDPI 2020-11-28 /pmc/articles/PMC7730831/ /pubmed/33260677 http://dx.doi.org/10.3390/s20236815 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 Yi, Cheng Lu, Dening Xie, Qian Xu, Jinxuan Wang, Jun Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud |
title | Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud |
title_full | Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud |
title_fullStr | Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud |
title_full_unstemmed | Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud |
title_short | Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud |
title_sort | tunnel deformation inspection via global spatial axis extraction from 3d raw point cloud |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730831/ https://www.ncbi.nlm.nih.gov/pubmed/33260677 http://dx.doi.org/10.3390/s20236815 |
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