Semantic mapping for autonomous subsea intervention

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based simultaneous localization and mapping (SLAM) and 3D ob...

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Detalles Bibliográficos
Autores: Vallicrosa Massaguer, Guillem, Himri, Khadidja, Ridao Rodríguez, Pere, Grácias, Nuno Ricardo Estrela
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/20029
Acceso en línea:http://hdl.handle.net/10256/20029
Access Level:acceso abierto
Palabra clave:Reconeixement de formes (Informàtica)
Pattern recognition systems
Robots autònoms
Autonomous robots
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spelling Semantic mapping for autonomous subsea interventionVallicrosa Massaguer, GuillemHimri, KhadidjaRidao Rodríguez, PereGrácias, Nuno Ricardo EstrelaReconeixement de formes (Informàtica)Pattern recognition systemsRobots autònomsAutonomous robotsThis paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based simultaneous localization and mapping (SLAM) and 3D object recognition using a database of a priori known objects. The robot uses Doppler velocity log (DVL), pressure, and attitude and heading reference system (AHRS) sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the SLAM, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The SLAM provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near futureThis work was supported by the Spanish Government through a FPI Ph.D. grant to K. Himri, as well as by the Spanish Project DPI2017-86372-C3-2-R (TWINBOT-GIRONA1000) and the European project H2020-INFRAIA-2017-1-twostage-731103 (EUMarineRobots)MDPI (Multidisciplinary Digital Publishing Institute)Agencia Estatal de Investigación2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/20029http://hdl.handle.net/10256/20029Sensors, 2021, vol. 21, núm. 20, p. 6740Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.3390/s21206740info:eu-repo/semantics/altIdentifier/eissn/1424-8220DPI2017-86372-C3-2-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86372-C3-2-Rinfo:eu-repo/grantAgreement/EC/H2020/731103Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/200292026-05-29T05:05:01Z
dc.title.none.fl_str_mv Semantic mapping for autonomous subsea intervention
title Semantic mapping for autonomous subsea intervention
spellingShingle Semantic mapping for autonomous subsea intervention
Vallicrosa Massaguer, Guillem
Reconeixement de formes (Informàtica)
Pattern recognition systems
Robots autònoms
Autonomous robots
title_short Semantic mapping for autonomous subsea intervention
title_full Semantic mapping for autonomous subsea intervention
title_fullStr Semantic mapping for autonomous subsea intervention
title_full_unstemmed Semantic mapping for autonomous subsea intervention
title_sort Semantic mapping for autonomous subsea intervention
dc.creator.none.fl_str_mv Vallicrosa Massaguer, Guillem
Himri, Khadidja
Ridao Rodríguez, Pere
Grácias, Nuno Ricardo Estrela
author Vallicrosa Massaguer, Guillem
author_facet Vallicrosa Massaguer, Guillem
Himri, Khadidja
Ridao Rodríguez, Pere
Grácias, Nuno Ricardo Estrela
author_role author
author2 Himri, Khadidja
Ridao Rodríguez, Pere
Grácias, Nuno Ricardo Estrela
author2_role author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Reconeixement de formes (Informàtica)
Pattern recognition systems
Robots autònoms
Autonomous robots
topic Reconeixement de formes (Informàtica)
Pattern recognition systems
Robots autònoms
Autonomous robots
description This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based simultaneous localization and mapping (SLAM) and 3D object recognition using a database of a priori known objects. The robot uses Doppler velocity log (DVL), pressure, and attitude and heading reference system (AHRS) sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the SLAM, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The SLAM provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/20029
http://hdl.handle.net/10256/20029
url http://hdl.handle.net/10256/20029
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/s21206740
info:eu-repo/semantics/altIdentifier/eissn/1424-8220
DPI2017-86372-C3-2-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86372-C3-2-R
info:eu-repo/grantAgreement/EC/H2020/731103
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI (Multidisciplinary Digital Publishing Institute)
publisher.none.fl_str_mv MDPI (Multidisciplinary Digital Publishing Institute)
dc.source.none.fl_str_mv Sensors, 2021, vol. 21, núm. 20, p. 6740
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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