Long-term mapping and localization using feature stability histograms

This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise t...

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Detalles Bibliográficos
Autores: Bacca Cortés, Eval Bladimir, Salvi, Joaquim, Cufí i Solé, Xavier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2013
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/11542
Acceso en línea:http://hdl.handle.net/10256/11542
Access Level:acceso embargado
Palabra clave:Robòtica
Robotics
Robots -- Sistemes de control
Robots -- Control systems
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spelling Long-term mapping and localization using feature stability histogramsBacca Cortés, Eval BladimirSalvi, JoaquimCufí i Solé, XavierRobòticaRoboticsRobots -- Sistemes de controlRobots -- Control systemsThis work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term runThis work has been partially supported by the project RAIMON—Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref. CTM2011-29691-C02-02) funded by the Spanish Ministry of Science and Innovation, the LASPAUCOLCIENCIAS grant 136-2008, the University of Valle contract 644-19-04-95, and the consolidated research group’s grant SGR2009-00380ElsevierMinisterio de Ciencia e Innovación (Espanya)Generalitat de Catalunya. Agència de Gestió d'Ajuts Universitaris i de Recercainfoinfo2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10256/11542http://hdl.handle.net/10256/11542© Robotics and Autonomous Systems, 2013, vol. 61, núm. 12, p. 1539-1558Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.robot.2013.07.003info:eu-repo/semantics/altIdentifier/issn/0921-8890info:eu-repo/grantAgreement/MICINN//CTM2011-29691-C02-02AGAUR/2009-2014/2009 SGR-380Tots els drets reservatsinfo:eu-repo/semantics/embargoedAccessoai:recercat.cat:10256/115422026-05-29T05:05:01Z
dc.title.none.fl_str_mv Long-term mapping and localization using feature stability histograms
title Long-term mapping and localization using feature stability histograms
spellingShingle Long-term mapping and localization using feature stability histograms
Bacca Cortés, Eval Bladimir
Robòtica
Robotics
Robots -- Sistemes de control
Robots -- Control systems
title_short Long-term mapping and localization using feature stability histograms
title_full Long-term mapping and localization using feature stability histograms
title_fullStr Long-term mapping and localization using feature stability histograms
title_full_unstemmed Long-term mapping and localization using feature stability histograms
title_sort Long-term mapping and localization using feature stability histograms
dc.creator.none.fl_str_mv Bacca Cortés, Eval Bladimir
Salvi, Joaquim
Cufí i Solé, Xavier
author Bacca Cortés, Eval Bladimir
author_facet Bacca Cortés, Eval Bladimir
Salvi, Joaquim
Cufí i Solé, Xavier
author_role author
author2 Salvi, Joaquim
Cufí i Solé, Xavier
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (Espanya)
Generalitat de Catalunya. Agència de Gestió d'Ajuts Universitaris i de Recerca
dc.subject.none.fl_str_mv Robòtica
Robotics
Robots -- Sistemes de control
Robots -- Control systems
topic Robòtica
Robotics
Robots -- Sistemes de control
Robots -- Control systems
description This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term run
publishDate 2013
dc.date.none.fl_str_mv 2013
info
info
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/11542
http://hdl.handle.net/10256/11542
url http://hdl.handle.net/10256/11542
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.robot.2013.07.003
info:eu-repo/semantics/altIdentifier/issn/0921-8890
info:eu-repo/grantAgreement/MICINN//CTM2011-29691-C02-02
AGAUR/2009-2014/2009 SGR-380
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv © Robotics and Autonomous Systems, 2013, vol. 61, núm. 12, p. 1539-1558
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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