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Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System
Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960621/ https://www.ncbi.nlm.nih.gov/pubmed/31817186 http://dx.doi.org/10.3390/s19245345 |
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author | Aijazi, Ahmad K. Malaterre, Laurent Trassoudaine, Laurent Chateau, Thierry Checchin, Paul |
author_facet | Aijazi, Ahmad K. Malaterre, Laurent Trassoudaine, Laurent Chateau, Thierry Checchin, Paul |
author_sort | Aijazi, Ahmad K. |
collection | PubMed |
description | Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than [Formula: see text] , an overall segmentation accuracy of [Formula: see text] and an overall modeling error of less than [Formula: see text]. The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution. |
format | Online Article Text |
id | pubmed-6960621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69606212020-01-23 Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System Aijazi, Ahmad K. Malaterre, Laurent Trassoudaine, Laurent Chateau, Thierry Checchin, Paul Sensors (Basel) Article Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than [Formula: see text] , an overall segmentation accuracy of [Formula: see text] and an overall modeling error of less than [Formula: see text]. The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution. MDPI 2019-12-04 /pmc/articles/PMC6960621/ /pubmed/31817186 http://dx.doi.org/10.3390/s19245345 Text en © 2019 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 Aijazi, Ahmad K. Malaterre, Laurent Trassoudaine, Laurent Chateau, Thierry Checchin, Paul Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System |
title | Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System |
title_full | Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System |
title_fullStr | Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System |
title_full_unstemmed | Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System |
title_short | Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System |
title_sort | automatic detection and modeling of underground pipes using a portable 3d lidar system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960621/ https://www.ncbi.nlm.nih.gov/pubmed/31817186 http://dx.doi.org/10.3390/s19245345 |
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