Evaluation of three vision based object perception methods for a mobile robot

This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: i...

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Detalles Bibliográficos
Autores: Ramisa, Arnau, Aldavert, David, Vasudevan, Shrihari, Toledo, Ricardo, López de Mántaras, Ramón
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/96499
Acceso en línea:http://hdl.handle.net/10261/96499
Access Level:acceso abierto
Palabra clave:Mobile robots
Object recognition
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spelling Evaluation of three vision based object perception methods for a mobile robotRamisa, ArnauAldavert, DavidVasudevan, ShrihariToledo, RicardoLópez de Mántaras, RamónMobile robotsObject recognitionThis paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings. © Springer Science+Business Media B.V. 2012.This work was supported by the following grants: JAE Doc of the CSIC, FEDER European Social funds, AGAUR grant 2009-SGR-1434, the Government of Spain under research programme Consolider Ingenio 2 010: MIPRCV (CSD2007-00018) and MICINN project TIN2011-25606 (SiMeVé), Rio Tinto Centre for Mine Automation, and the ARC Centre of Excellence programme, funded by the Australian Research Council and the New South Wales State Government.Peer ReviewedSpringer NatureConsejo Superior de Investigaciones Científicas (España)European CommissionMinisterio de Economía y Competitividad (España)Australian Research CouncilAustralian Government2014201420122014info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/96499reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1007/s10846-012-9675-8info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/964992026-05-22T06:33:51Z
dc.title.none.fl_str_mv Evaluation of three vision based object perception methods for a mobile robot
title Evaluation of three vision based object perception methods for a mobile robot
spellingShingle Evaluation of three vision based object perception methods for a mobile robot
Ramisa, Arnau
Mobile robots
Object recognition
title_short Evaluation of three vision based object perception methods for a mobile robot
title_full Evaluation of three vision based object perception methods for a mobile robot
title_fullStr Evaluation of three vision based object perception methods for a mobile robot
title_full_unstemmed Evaluation of three vision based object perception methods for a mobile robot
title_sort Evaluation of three vision based object perception methods for a mobile robot
dc.creator.none.fl_str_mv Ramisa, Arnau
Aldavert, David
Vasudevan, Shrihari
Toledo, Ricardo
López de Mántaras, Ramón
author Ramisa, Arnau
author_facet Ramisa, Arnau
Aldavert, David
Vasudevan, Shrihari
Toledo, Ricardo
López de Mántaras, Ramón
author_role author
author2 Aldavert, David
Vasudevan, Shrihari
Toledo, Ricardo
López de Mántaras, Ramón
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas (España)
European Commission
Ministerio de Economía y Competitividad (España)
Australian Research Council
Australian Government
dc.subject.none.fl_str_mv Mobile robots
Object recognition
topic Mobile robots
Object recognition
description This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings. © Springer Science+Business Media B.V. 2012.
publishDate 2012
dc.date.none.fl_str_mv 2012
2014
2014
2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/96499
url http://hdl.handle.net/10261/96499
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1007/s10846-012-9675-8
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
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
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repository.mail.fl_str_mv
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score 15,812455

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