Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas

Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the A...

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Detalles Bibliográficos
Autores: Ramisa, Arnau, Goldhoorn, Alex, Aldavert, David, Toledo, Ricardo, López de Mántaras, Ramón
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2011
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/138217
Acceso en línea:http://hdl.handle.net/10261/138217
Access Level:acceso abierto
Palabra clave:Biologically inspired methods
Robot navigation
Local features
Visual homing
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spelling Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramasRamisa, ArnauGoldhoorn, AlexAldavert, DavidToledo, RicardoLópez de Mántaras, RamónBiologically inspired methodsRobot navigationLocal featuresVisual homingBiologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. © 2011 Springer Science+Business Media B.V.This work was partially supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, the MID-CBR project grant TIN2006-15140- C03-01 and FEDER funds, the grant 2005-SGR-00093, the MIPRCV Consolider Imagennio 2010 and the Marco Polo fund from the University of Groningen.Peer ReviewedKluwer Academic PublishersUniversity of GroningenConsejo Superior de Investigaciones Científicas (España)Generalitat de CatalunyaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2016201620112016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/138217reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1382172026-05-22T06:33:51Z
dc.title.none.fl_str_mv Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
title Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
spellingShingle Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
Ramisa, Arnau
Biologically inspired methods
Robot navigation
Local features
Visual homing
title_short Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
title_full Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
title_fullStr Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
title_full_unstemmed Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
title_sort Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas
dc.creator.none.fl_str_mv Ramisa, Arnau
Goldhoorn, Alex
Aldavert, David
Toledo, Ricardo
López de Mántaras, Ramón
author Ramisa, Arnau
author_facet Ramisa, Arnau
Goldhoorn, Alex
Aldavert, David
Toledo, Ricardo
López de Mántaras, Ramón
author_role author
author2 Goldhoorn, Alex
Aldavert, David
Toledo, Ricardo
López de Mántaras, Ramón
author2_role author
author
author
author
dc.contributor.none.fl_str_mv University of Groningen
Consejo Superior de Investigaciones Científicas (España)
Generalitat de Catalunya
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Biologically inspired methods
Robot navigation
Local features
Visual homing
topic Biologically inspired methods
Robot navigation
Local features
Visual homing
description Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. © 2011 Springer Science+Business Media B.V.
publishDate 2011
dc.date.none.fl_str_mv 2011
2016
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/138217
url http://hdl.handle.net/10261/138217
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Kluwer Academic Publishers
publisher.none.fl_str_mv Kluwer Academic Publishers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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