Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter

The computational bottleneck in all information-based algorithms for simultaneous localization and mapping (SLAM) is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. In general, recover...

ver descrição completa

Detalhes bibliográficos
Autores: Ila, Viorela Simona, Porta Pleite, Josep Maria|||0000-0002-5056-1717, Andrade-Cetto, Juan|||0000-0002-6354-8941
Formato: artículo
Fecha de publicación:2011
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/14111
Acesso em linha:https://hdl.handle.net/2117/14111
https://dx.doi.org/10.1016/j.robot.2011.02.010
Access Level:acceso abierto
Palavra-chave:Simultaneous localization and mapping
Pose SLAM
Hierarchical SLAM
Kalman filter
Information filter
SLAM (robòtica)
Àrees temàtiques de la UPC::Informàtica::Robòtica
id ES_85f8a1a31df31facf6ef9cc1e45163b4
oai_identifier_str oai:upcommons.upc.edu:2117/14111
network_acronym_str ES
network_name_str España
repository_id_str
spelling Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filterIla, Viorela SimonaPorta Pleite, Josep Maria|||0000-0002-5056-1717Andrade-Cetto, Juan|||0000-0002-6354-8941Simultaneous localization and mappingPose SLAMHierarchical SLAMKalman filterInformation filterPose SLAMSLAM (robòtica)Àrees temàtiques de la UPC::Informàtica::RobòticaThe computational bottleneck in all information-based algorithms for simultaneous localization and mapping (SLAM) is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. In general, recovering the state mean and covariance requires the inversion of a matrix with the size of the state, which is computationally too expensive in time and memory for large problems. Exactly sparse state representations, such as that of Pose SLAM, alleviate the cost of state recovery either in time or in memory, but not in both. In this paper, we present an approach to state estimation that is linear both in execution time and in memory footprint at loop closure, and constant otherwise. The method relies on a state representation that combines the Kalman and the information-based approaches. The strategy is valid for any SLAM system that maintains constraints between marginal states at different time slices. This includes both Pose SLAM, the variant of SLAM where only the robot trajectory is estimated, and hierarchical techniques in which submaps are registered with a network of relative geometric constraints.Peer Reviewed20112011-01-0120112011-11-29journal articlehttp://purl.org/coar/resource_type/c_6501AOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/14111https://dx.doi.org/10.1016/j.robot.2011.02.010reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/141112026-05-27T15:37:01Z
dc.title.none.fl_str_mv Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
title Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
spellingShingle Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
Ila, Viorela Simona
Simultaneous localization and mapping
Pose SLAM
Hierarchical SLAM
Kalman filter
Information filter
Pose SLAM
SLAM (robòtica)
Àrees temàtiques de la UPC::Informàtica::Robòtica
title_short Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
title_full Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
title_fullStr Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
title_full_unstemmed Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
title_sort Amortized constant time estimation in Pose SLAM and hierarchical SLAM using a mixed Kalman-information filter
dc.creator.none.fl_str_mv Ila, Viorela Simona
Porta Pleite, Josep Maria|||0000-0002-5056-1717
Andrade-Cetto, Juan|||0000-0002-6354-8941
author Ila, Viorela Simona
author_facet Ila, Viorela Simona
Porta Pleite, Josep Maria|||0000-0002-5056-1717
Andrade-Cetto, Juan|||0000-0002-6354-8941
author_role author
author2 Porta Pleite, Josep Maria|||0000-0002-5056-1717
Andrade-Cetto, Juan|||0000-0002-6354-8941
author2_role author
author
dc.subject.none.fl_str_mv Simultaneous localization and mapping
Pose SLAM
Hierarchical SLAM
Kalman filter
Information filter
Pose SLAM
SLAM (robòtica)
Àrees temàtiques de la UPC::Informàtica::Robòtica
topic Simultaneous localization and mapping
Pose SLAM
Hierarchical SLAM
Kalman filter
Information filter
Pose SLAM
SLAM (robòtica)
Àrees temàtiques de la UPC::Informàtica::Robòtica
description The computational bottleneck in all information-based algorithms for simultaneous localization and mapping (SLAM) is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. In general, recovering the state mean and covariance requires the inversion of a matrix with the size of the state, which is computationally too expensive in time and memory for large problems. Exactly sparse state representations, such as that of Pose SLAM, alleviate the cost of state recovery either in time or in memory, but not in both. In this paper, we present an approach to state estimation that is linear both in execution time and in memory footprint at loop closure, and constant otherwise. The method relies on a state representation that combines the Kalman and the information-based approaches. The strategy is valid for any SLAM system that maintains constraints between marginal states at different time slices. This includes both Pose SLAM, the variant of SLAM where only the robot trajectory is estimated, and hierarchical techniques in which submaps are registered with a network of relative geometric constraints.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-01-01
2011
2011-11-29
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/14111
https://dx.doi.org/10.1016/j.robot.2011.02.010
url https://hdl.handle.net/2117/14111
https://dx.doi.org/10.1016/j.robot.2011.02.010
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412339655114752
score 15,301603

Registos relacionados