Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

Autores
Auat Cheein, Fernando Alfredo; Pereira, Fernando M. Lobo; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.
Fil: Auat Cheein, Fernando Alfredo. Universidad Técnica Federico Santa María; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pereira, Fernando M. Lobo. Universidad de Porto; Portugal
Fil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Materia
SLAM
Monte Carlo uncertainty
Mobile robots
Map based navigation
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:11336/188878
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigationAuat Cheein, Fernando AlfredoPereira, Fernando M. LoboDi Sciascio, Fernando AgustínCarelli Albarracin, Ricardo OscarSLAMMonte Carlo uncertaintyMobile robotsMap based navigationhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.Fil: Auat Cheein, Fernando Alfredo. Universidad Técnica Federico Santa María; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pereira, Fernando M. Lobo. Universidad de Porto; PortugalFil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaCambridge University Press2012-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/188878Auat Cheein, Fernando Alfredo; Pereira, Fernando M. Lobo; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation; Cambridge University Press; Knowledge Engineering Review; 28; 1; 11-2012; 35-570269-8889CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1017/S0269888912000276info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/autonomous-simultaneous-localization-and-mapping-driven-by-monte-carlo-uncertainty-mapsbased-navigation/94978639E1F2C4A1DF8D162A46738D44info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:15:02Zoai:ri.conicet.gov.ar:11336/188878instacron: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 10:15:03.172CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
title Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
spellingShingle Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
Auat Cheein, Fernando Alfredo
SLAM
Monte Carlo uncertainty
Mobile robots
Map based navigation
title_short Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
title_full Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
title_fullStr Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
title_full_unstemmed Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
title_sort Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation
dc.creator.none.fl_str_mv Auat Cheein, Fernando Alfredo
Pereira, Fernando M. Lobo
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
author Auat Cheein, Fernando Alfredo
author_facet Auat Cheein, Fernando Alfredo
Pereira, Fernando M. Lobo
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Pereira, Fernando M. Lobo
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
author2_role author
author
author
dc.subject.none.fl_str_mv SLAM
Monte Carlo uncertainty
Mobile robots
Map based navigation
topic SLAM
Monte Carlo uncertainty
Mobile robots
Map based navigation
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 addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.
Fil: Auat Cheein, Fernando Alfredo. Universidad Técnica Federico Santa María; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pereira, Fernando M. Lobo. Universidad de Porto; Portugal
Fil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
description This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.
publishDate 2012
dc.date.none.fl_str_mv 2012-11
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/11336/188878
Auat Cheein, Fernando Alfredo; Pereira, Fernando M. Lobo; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation; Cambridge University Press; Knowledge Engineering Review; 28; 1; 11-2012; 35-57
0269-8889
CONICET Digital
CONICET
url http://hdl.handle.net/11336/188878
identifier_str_mv Auat Cheein, Fernando Alfredo; Pereira, Fernando M. Lobo; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation; Cambridge University Press; Knowledge Engineering Review; 28; 1; 11-2012; 35-57
0269-8889
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1017/S0269888912000276
info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/autonomous-simultaneous-localization-and-mapping-driven-by-monte-carlo-uncertainty-mapsbased-navigation/94978639E1F2C4A1DF8D162A46738D44
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
dc.publisher.none.fl_str_mv Cambridge University Press
publisher.none.fl_str_mv Cambridge University Press
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
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score 13.070432

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