Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/4247 http://dx.doi.org/10.1016/j.robot.2015.09.030 |
Resumo: | Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1 degrees-3 degrees degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise. |
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Robust 3/6 DoF self-localization system with selective map update for mobile robot platformsMobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1 degrees-3 degrees degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise.2017-12-18T18:30:20Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4247http://dx.doi.org/10.1016/j.robot.2015.09.030engCarlos Miguel CostaHéber Miguel SobreiraArmando SousaGermano Veigainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:37Zoai:repositorio.inesctec.pt:123456789/4247Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:24.397786Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
title |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
spellingShingle |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms Carlos Miguel Costa |
title_short |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
title_full |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
title_fullStr |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
title_full_unstemmed |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
title_sort |
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms |
author |
Carlos Miguel Costa |
author_facet |
Carlos Miguel Costa Héber Miguel Sobreira Armando Sousa Germano Veiga |
author_role |
author |
author2 |
Héber Miguel Sobreira Armando Sousa Germano Veiga |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Carlos Miguel Costa Héber Miguel Sobreira Armando Sousa Germano Veiga |
description |
Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1 degrees-3 degrees degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01T00:00:00Z 2016 2017-12-18T18:30:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/4247 http://dx.doi.org/10.1016/j.robot.2015.09.030 |
url |
http://repositorio.inesctec.pt/handle/123456789/4247 http://dx.doi.org/10.1016/j.robot.2015.09.030 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131608349933568 |