Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (R...

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Detalles Bibliográficos
Autores: Pérez Grau, Francisco Javier, Caballero Benítez, Fernando, Viguria, Antidio, Ollero Baturone, Aníbal
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/111275
Acceso en línea:https://hdl.handle.net/11441/111275
https://doi.org/10.1177/1729881417732757
Access Level:acceso abierto
Palabra clave:Aerial robotics
Localization
Autonomous vehicle navigation
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spelling Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigationPérez Grau, Francisco JavierCaballero Benítez, FernandoViguria, AntidioOllero Baturone, AníbalAerial roboticsLocalizationAutonomous vehicle navigationThis article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.Unión Europea (EuRoC project - EU FP7 ) CPIP 608849Gobierno de España (OCELLIMAV project) TEC2014-61708-EXPSAGE PublicationsIngeniería de Sistemas y AutomáticaEuropean Union (UE). FP7Gobierno de España2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/111275https://doi.org/10.1177/1729881417732757reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternational Journal of Advanced Robotic Systems, 14 (5), 1-15.CPIP 608849TEC2014-61708-EXPhttps://doi.org/10.1177/1729881417732757info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1112752026-06-17T12:51:07Z
dc.title.none.fl_str_mv Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
title Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
spellingShingle Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
Pérez Grau, Francisco Javier
Aerial robotics
Localization
Autonomous vehicle navigation
title_short Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
title_full Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
title_fullStr Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
title_full_unstemmed Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
title_sort Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation
dc.creator.none.fl_str_mv Pérez Grau, Francisco Javier
Caballero Benítez, Fernando
Viguria, Antidio
Ollero Baturone, Aníbal
author Pérez Grau, Francisco Javier
author_facet Pérez Grau, Francisco Javier
Caballero Benítez, Fernando
Viguria, Antidio
Ollero Baturone, Aníbal
author_role author
author2 Caballero Benítez, Fernando
Viguria, Antidio
Ollero Baturone, Aníbal
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
European Union (UE). FP7
Gobierno de España
dc.subject.none.fl_str_mv Aerial robotics
Localization
Autonomous vehicle navigation
topic Aerial robotics
Localization
Autonomous vehicle navigation
description This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/111275
https://doi.org/10.1177/1729881417732757
url https://hdl.handle.net/11441/111275
https://doi.org/10.1177/1729881417732757
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Advanced Robotic Systems, 14 (5), 1-15.
CPIP 608849
TEC2014-61708-EXP
https://doi.org/10.1177/1729881417732757
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv SAGE Publications
publisher.none.fl_str_mv SAGE Publications
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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