Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context

Autores
Gimenez Romero, Javier Alejandro; Amicarelli, Adriana Natacha; Toibero, Juan Marcos; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.
Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Toibero, Juan Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Materia
Iterated Conditional Modes
Markov Random Fields
Modelling
On-Line Solver
Simultaneous Localization And Mapping
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/63541
Acceder
id CONICETDig_c27243329514e8163407a37652af4973
oai_identifier_str oai:ri.conicet.gov.ar:11336/63541
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields ContextGimenez Romero, Javier AlejandroAmicarelli, Adriana NatachaToibero, Juan MarcosDi Sciascio, Fernando AgustínCarelli Albarracin, Ricardo OscarIterated Conditional ModesMarkov Random FieldsModellingOn-Line SolverSimultaneous Localization And Mappinghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Toibero, Juan Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaSpringer2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/63541Gimenez Romero, Javier Alejandro; Amicarelli, Adriana Natacha; Toibero, Juan Marcos; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context; Springer; International Journal of Automation and Computing; 15; 3; 6-2018; 310-3241476-81861751-8520CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11633-017-1109-4info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11633-017-1109-4info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:43:39Zoai:ri.conicet.gov.ar:11336/63541instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:43:39.474CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
title Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
spellingShingle Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
Gimenez Romero, Javier Alejandro
Iterated Conditional Modes
Markov Random Fields
Modelling
On-Line Solver
Simultaneous Localization And Mapping
title_short Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
title_full Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
title_fullStr Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
title_full_unstemmed Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
title_sort Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
dc.creator.none.fl_str_mv Gimenez Romero, Javier Alejandro
Amicarelli, Adriana Natacha
Toibero, Juan Marcos
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
author Gimenez Romero, Javier Alejandro
author_facet Gimenez Romero, Javier Alejandro
Amicarelli, Adriana Natacha
Toibero, Juan Marcos
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Amicarelli, Adriana Natacha
Toibero, Juan Marcos
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
author2_role author
author
author
author
dc.subject.none.fl_str_mv Iterated Conditional Modes
Markov Random Fields
Modelling
On-Line Solver
Simultaneous Localization And Mapping
topic Iterated Conditional Modes
Markov Random Fields
Modelling
On-Line Solver
Simultaneous Localization And Mapping
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.
Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Toibero, Juan Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
description This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/63541
Gimenez Romero, Javier Alejandro; Amicarelli, Adriana Natacha; Toibero, Juan Marcos; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context; Springer; International Journal of Automation and Computing; 15; 3; 6-2018; 310-324
1476-8186
1751-8520
CONICET Digital
CONICET
url http://hdl.handle.net/11336/63541
identifier_str_mv Gimenez Romero, Javier Alejandro; Amicarelli, Adriana Natacha; Toibero, Juan Marcos; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context; Springer; International Journal of Automation and Computing; 15; 3; 6-2018; 310-324
1476-8186
1751-8520
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s11633-017-1109-4
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11633-017-1109-4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613373592862720
score 13.070432

Publicaciones similares