Enhanced PCA-based localization using depth maps with missing data
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , |
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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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) |
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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 |
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1799133408058671104 |