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Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information
This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961582/ https://www.ncbi.nlm.nih.gov/pubmed/33807708 http://dx.doi.org/10.3390/s21051807 |
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author | Himri, Khadidja Ridao, Pere Gracias, Nuno |
author_facet | Himri, Khadidja Ridao, Pere Gracias, Nuno |
author_sort | Himri, Khadidja |
collection | PubMed |
description | This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of [Formula: see text] , [Formula: see text] and [Formula: see text] , respectively, clearly showing the advantages of using the Bayesian estimation ([Formula: see text] increase) and the inclusion of semantic information ([Formula: see text] further increase). |
format | Online Article Text |
id | pubmed-7961582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79615822021-03-17 Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information Himri, Khadidja Ridao, Pere Gracias, Nuno Sensors (Basel) Article This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of [Formula: see text] , [Formula: see text] and [Formula: see text] , respectively, clearly showing the advantages of using the Bayesian estimation ([Formula: see text] increase) and the inclusion of semantic information ([Formula: see text] further increase). MDPI 2021-03-05 /pmc/articles/PMC7961582/ /pubmed/33807708 http://dx.doi.org/10.3390/s21051807 Text en © 2021 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 Himri, Khadidja Ridao, Pere Gracias, Nuno Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information |
title | Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information |
title_full | Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information |
title_fullStr | Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information |
title_full_unstemmed | Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information |
title_short | Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information |
title_sort | underwater object recognition using point-features, bayesian estimation and semantic information |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961582/ https://www.ncbi.nlm.nih.gov/pubmed/33807708 http://dx.doi.org/10.3390/s21051807 |
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