Mapping dynamic environments using Markov random field models

Detalhes bibliográficos
Autor(a) principal: Li, Hongjun
Data de Publicação: 2018
Outros Autores: Barão, Miguel, Rato, Luis
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/27573
https://doi.org/10.23919/IConAC.2018.8749092
Resumo: This paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are used to represent the dynamic environment. The state change of every grid cell is modelled by an HMM with an unknown transition matrix. MRFs are applied to consider the dependence between different transition matrices. The unknown parameters are learnt from not only the corresponding observations but also its neighbours. Given the dependence, parameter maps are smooth. Expectation maximization (EM) is applied to obtain the best parameters from observations. Finally, a simulation is done to evaluate the proposed method.
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spelling Mapping dynamic environments using Markov random field modelsMappingMarkovdynamicRoboticThis paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are used to represent the dynamic environment. The state change of every grid cell is modelled by an HMM with an unknown transition matrix. MRFs are applied to consider the dependence between different transition matrices. The unknown parameters are learnt from not only the corresponding observations but also its neighbours. Given the dependence, parameter maps are smooth. Expectation maximization (EM) is applied to obtain the best parameters from observations. Finally, a simulation is done to evaluate the proposed method.IEEE2020-03-02T14:04:13Z2020-03-022018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/27573https://doi.org/10.23919/IConAC.2018.8749092http://hdl.handle.net/10174/27573https://doi.org/10.23919/IConAC.2018.8749092engH. Li, M. Barão and L. Rato, "Mapping dynamic environments using Markov random field models," 2018 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, United Kingdom, 2018, pp. 1-5.ndmjsb@uevora.ptlmr@uevora.pt498Li, HongjunBarão, MiguelRato, Luisinfo: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:RCAAP2024-01-03T19:23:13Zoai:dspace.uevora.pt:10174/27573Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:17:39.375826Repositório 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 Mapping dynamic environments using Markov random field models
title Mapping dynamic environments using Markov random field models
spellingShingle Mapping dynamic environments using Markov random field models
Li, Hongjun
Mapping
Markov
dynamic
Robotic
title_short Mapping dynamic environments using Markov random field models
title_full Mapping dynamic environments using Markov random field models
title_fullStr Mapping dynamic environments using Markov random field models
title_full_unstemmed Mapping dynamic environments using Markov random field models
title_sort Mapping dynamic environments using Markov random field models
author Li, Hongjun
author_facet Li, Hongjun
Barão, Miguel
Rato, Luis
author_role author
author2 Barão, Miguel
Rato, Luis
author2_role author
author
dc.contributor.author.fl_str_mv Li, Hongjun
Barão, Miguel
Rato, Luis
dc.subject.por.fl_str_mv Mapping
Markov
dynamic
Robotic
topic Mapping
Markov
dynamic
Robotic
description This paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are used to represent the dynamic environment. The state change of every grid cell is modelled by an HMM with an unknown transition matrix. MRFs are applied to consider the dependence between different transition matrices. The unknown parameters are learnt from not only the corresponding observations but also its neighbours. Given the dependence, parameter maps are smooth. Expectation maximization (EM) is applied to obtain the best parameters from observations. Finally, a simulation is done to evaluate the proposed method.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01T00:00:00Z
2020-03-02T14:04:13Z
2020-03-02
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/10174/27573
https://doi.org/10.23919/IConAC.2018.8749092
http://hdl.handle.net/10174/27573
https://doi.org/10.23919/IConAC.2018.8749092
url http://hdl.handle.net/10174/27573
https://doi.org/10.23919/IConAC.2018.8749092
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv H. Li, M. Barão and L. Rato, "Mapping dynamic environments using Markov random field models," 2018 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, United Kingdom, 2018, pp. 1-5.
nd
mjsb@uevora.pt
lmr@uevora.pt
498
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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)
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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