Enhanced PCA-based localization using depth maps with missing data

Detalhes bibliográficos
Autor(a) principal: Carreira, Fernando
Data de Publicação: 2015
Outros Autores: Calado, João Manuel Ferreira, Cardeira, Carlos, Oliveira, Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/5809
Resumo: In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.
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spelling Enhanced PCA-based localization using depth maps with missing dataMobile robotsRobot sensing systemsSensor fusionPrincipal component analysisKalman filtersIn this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.SpringerRCIPLCarreira, FernandoCalado, João Manuel FerreiraCardeira, CarlosOliveira, Paulo2016-03-08T15:49:30Z2015-022015-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/5809engCARREIRA, Fernando; [et al.] - Enhanced PCA-based localization using depth maps with missing data. Journal of Intelligent & Robotics Systems. ISSN. 0921-0296. Vol. 77, N.º 2, SI (2015), pp. 341-3600921-029610.1007/s10846-013-0013-6metadata only accessinfo: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:RCAAP2023-08-03T09:49:45Zoai:repositorio.ipl.pt:10400.21/5809Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:15:02.390989Repositório 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 Enhanced PCA-based localization using depth maps with missing data
title Enhanced PCA-based localization using depth maps with missing data
spellingShingle Enhanced PCA-based localization using depth maps with missing data
Carreira, Fernando
Mobile robots
Robot sensing systems
Sensor fusion
Principal component analysis
Kalman filters
title_short Enhanced PCA-based localization using depth maps with missing data
title_full Enhanced PCA-based localization using depth maps with missing data
title_fullStr Enhanced PCA-based localization using depth maps with missing data
title_full_unstemmed Enhanced PCA-based localization using depth maps with missing data
title_sort Enhanced PCA-based localization using depth maps with missing data
author Carreira, Fernando
author_facet Carreira, Fernando
Calado, João Manuel Ferreira
Cardeira, Carlos
Oliveira, Paulo
author_role author
author2 Calado, João Manuel Ferreira
Cardeira, Carlos
Oliveira, Paulo
author2_role author
author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Carreira, Fernando
Calado, João Manuel Ferreira
Cardeira, Carlos
Oliveira, Paulo
dc.subject.por.fl_str_mv Mobile robots
Robot sensing systems
Sensor fusion
Principal component analysis
Kalman filters
topic Mobile robots
Robot sensing systems
Sensor fusion
Principal component analysis
Kalman filters
description In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.
publishDate 2015
dc.date.none.fl_str_mv 2015-02
2015-02-01T00:00:00Z
2016-03-08T15:49:30Z
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://hdl.handle.net/10400.21/5809
url http://hdl.handle.net/10400.21/5809
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv CARREIRA, Fernando; [et al.] - Enhanced PCA-based localization using depth maps with missing data. Journal of Intelligent & Robotics Systems. ISSN. 0921-0296. Vol. 77, N.º 2, SI (2015), pp. 341-360
0921-0296
10.1007/s10846-013-0013-6
dc.rights.driver.fl_str_mv metadata only access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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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