Combining sparse and dense methods in 6D Visual Odometry

Detalhes bibliográficos
Autor(a) principal: Silva, Hugo Miguel
Data de Publicação: 2013
Outros Autores: Silva, Eduardo, Bernardino, Alexandre
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/7290
Resumo: 13th International Conference on Autonomous Robot Systems (Robotica), 2013, Lisboa
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spelling Combining sparse and dense methods in 6D Visual Odometry5-point RANSAC algorithm6D visual odometry probabilistic approachProcrustes methodAbsolute orientation methodDense methodDense optical flow methods13th International Conference on Autonomous Robot Systems (Robotica), 2013, LisboaVisual Odometry is one of the most powerful, yet challenging, means of estimating robot ego-motion. By grounding perception to the static features in the environment, vision is able, in principle, to prevent the estimation bias rather common in other sensory modalities such as inertial measurement units or wheel odometers. We present a novel approach to ego-motion estimation of a mobile robot by using a 6D Visual Odometry Probabilistic Approach. Our approach exploits the complementarity of dense optical flow methods and sparse feature based methods to achieve 6D estimation of vehicle motion. A dense probabilistic method is used to robustly estimate the epipolar geometry between two consecutive stereo pairs; a sparse feature stereo approach to estimate feature depth; and an Absolute Orientation method like the Procrustes to estimate the global scale factor. We tested our proposed method on a known dataset and compared our 6D Visual Odometry Probabilistic Approach without filtering techniques against a implementation that uses the well known 5-point RANSAC algorithm. Moreover, comparison with an Inertial Measurement Unit (RTK-GPS) is also performed, for providing a more detailed evaluation of the method against ground-truth information.IEEERepositório Científico do Instituto Politécnico do PortoSilva, Hugo MiguelSilva, EduardoBernardino, Alexandre2015-12-29T11:11:06Z20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/7290eng978-1-4799-1246-910.1109/Robotica.2013.6623527metadata 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:RCAAP2023-03-13T12:47:29Zoai:recipp.ipp.pt:10400.22/7290Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:27:37.127107Repositório 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 Combining sparse and dense methods in 6D Visual Odometry
title Combining sparse and dense methods in 6D Visual Odometry
spellingShingle Combining sparse and dense methods in 6D Visual Odometry
Silva, Hugo Miguel
5-point RANSAC algorithm
6D visual odometry probabilistic approach
Procrustes method
Absolute orientation method
Dense method
Dense optical flow methods
title_short Combining sparse and dense methods in 6D Visual Odometry
title_full Combining sparse and dense methods in 6D Visual Odometry
title_fullStr Combining sparse and dense methods in 6D Visual Odometry
title_full_unstemmed Combining sparse and dense methods in 6D Visual Odometry
title_sort Combining sparse and dense methods in 6D Visual Odometry
author Silva, Hugo Miguel
author_facet Silva, Hugo Miguel
Silva, Eduardo
Bernardino, Alexandre
author_role author
author2 Silva, Eduardo
Bernardino, Alexandre
author2_role author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Silva, Hugo Miguel
Silva, Eduardo
Bernardino, Alexandre
dc.subject.por.fl_str_mv 5-point RANSAC algorithm
6D visual odometry probabilistic approach
Procrustes method
Absolute orientation method
Dense method
Dense optical flow methods
topic 5-point RANSAC algorithm
6D visual odometry probabilistic approach
Procrustes method
Absolute orientation method
Dense method
Dense optical flow methods
description 13th International Conference on Autonomous Robot Systems (Robotica), 2013, Lisboa
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01T00:00:00Z
2015-12-29T11:11:06Z
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/10400.22/7290
url http://hdl.handle.net/10400.22/7290
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-1-4799-1246-9
10.1109/Robotica.2013.6623527
dc.rights.driver.fl_str_mv metadata only access
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rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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
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