Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm

Autores
Auat Cheein, Fernando Alfredo; Carelli Albarracin, Ricardo Oscar
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper introduces several non-arbitrary features selection techniques for aSimultaneous Localization and Mapping (SLAM) algorithm. The features selection criteriaare based on the determination of the most significant features from a SLAM convergenceperspective. The SLAM algorithm implemented in this work is a sequential EKF (ExtendedKalman filter) SLAM. The features selection criteria are applied on the correction stage ofthe SLAM algorithm, restricting it to correct the SLAM algorithm with the most significantfeatures. This restriction also causes a decrement in the processing time of the SLAM.Several experiments with a mobile robot are shown in this work. The experiments concernthe maps reconstruction and a comparison between the different proposed techniques performance.The experiments were carried out at an outdoor environment composed by trees,although the results shown herein are not restricted to a special type of features.
Fil: Auat Cheein, Fernando Alfredo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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; Argentina
Materia
SLAM
Mapping
Features Selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/243645
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM AlgorithmAuat Cheein, Fernando AlfredoCarelli Albarracin, Ricardo OscarSLAMMappingFeatures Selectionhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper introduces several non-arbitrary features selection techniques for aSimultaneous Localization and Mapping (SLAM) algorithm. The features selection criteriaare based on the determination of the most significant features from a SLAM convergenceperspective. The SLAM algorithm implemented in this work is a sequential EKF (ExtendedKalman filter) SLAM. The features selection criteria are applied on the correction stage ofthe SLAM algorithm, restricting it to correct the SLAM algorithm with the most significantfeatures. This restriction also causes a decrement in the processing time of the SLAM.Several experiments with a mobile robot are shown in this work. The experiments concernthe maps reconstruction and a comparison between the different proposed techniques performance.The experiments were carried out at an outdoor environment composed by trees,although the results shown herein are not restricted to a special type of features.Fil: Auat Cheein, Fernando Alfredo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMolecular Diversity Preservation International2010-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/243645Auat Cheein, Fernando Alfredo; Carelli Albarracin, Ricardo Oscar; Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm; Molecular Diversity Preservation International; Sensors; 11; 1; 12-2010; 62-891424-8220CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1424-8220/11/1/62/info:eu-repo/semantics/altIdentifier/doi/10.3390/s110100062info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:46:03Zoai:ri.conicet.gov.ar:11336/243645instacron: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:34982025-09-03 09:46:03.991CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
title Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
spellingShingle Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
Auat Cheein, Fernando Alfredo
SLAM
Mapping
Features Selection
title_short Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
title_full Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
title_fullStr Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
title_full_unstemmed Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
title_sort Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm
dc.creator.none.fl_str_mv Auat Cheein, Fernando Alfredo
Carelli Albarracin, Ricardo Oscar
author Auat Cheein, Fernando Alfredo
author_facet Auat Cheein, Fernando Alfredo
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Carelli Albarracin, Ricardo Oscar
author2_role author
dc.subject.none.fl_str_mv SLAM
Mapping
Features Selection
topic SLAM
Mapping
Features Selection
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 introduces several non-arbitrary features selection techniques for aSimultaneous Localization and Mapping (SLAM) algorithm. The features selection criteriaare based on the determination of the most significant features from a SLAM convergenceperspective. The SLAM algorithm implemented in this work is a sequential EKF (ExtendedKalman filter) SLAM. The features selection criteria are applied on the correction stage ofthe SLAM algorithm, restricting it to correct the SLAM algorithm with the most significantfeatures. This restriction also causes a decrement in the processing time of the SLAM.Several experiments with a mobile robot are shown in this work. The experiments concernthe maps reconstruction and a comparison between the different proposed techniques performance.The experiments were carried out at an outdoor environment composed by trees,although the results shown herein are not restricted to a special type of features.
Fil: Auat Cheein, Fernando Alfredo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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; Argentina
description This paper introduces several non-arbitrary features selection techniques for aSimultaneous Localization and Mapping (SLAM) algorithm. The features selection criteriaare based on the determination of the most significant features from a SLAM convergenceperspective. The SLAM algorithm implemented in this work is a sequential EKF (ExtendedKalman filter) SLAM. The features selection criteria are applied on the correction stage ofthe SLAM algorithm, restricting it to correct the SLAM algorithm with the most significantfeatures. This restriction also causes a decrement in the processing time of the SLAM.Several experiments with a mobile robot are shown in this work. The experiments concernthe maps reconstruction and a comparison between the different proposed techniques performance.The experiments were carried out at an outdoor environment composed by trees,although the results shown herein are not restricted to a special type of features.
publishDate 2010
dc.date.none.fl_str_mv 2010-12
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/11336/243645
Auat Cheein, Fernando Alfredo; Carelli Albarracin, Ricardo Oscar; Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm; Molecular Diversity Preservation International; Sensors; 11; 1; 12-2010; 62-89
1424-8220
CONICET Digital
CONICET
url http://hdl.handle.net/11336/243645
identifier_str_mv Auat Cheein, Fernando Alfredo; Carelli Albarracin, Ricardo Oscar; Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm; Molecular Diversity Preservation International; Sensors; 11; 1; 12-2010; 62-89
1424-8220
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1424-8220/11/1/62/
info:eu-repo/semantics/altIdentifier/doi/10.3390/s110100062
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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_ 1842268771636477952
score 13.13397

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