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Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

2015-02Journal article Restricted access
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dc.contributor.authorCampos, Francisco M.
dc.contributor.authorCorreia, Luís
dc.contributor.authorCalado, João Manuel Ferreira
dc.date.accessioned2016-04-19T12:21:19Z
dc.date.available2016-04-19T12:21:19Z
dc.date.issued2015-02
dc.description.abstractIn the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.pt_PT
dc.identifier.citationCAMPOS, Francisco Marnoto; [et al] - Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches. Journal of Intelligent & Robotics Systems. ISSN 0921-0296. Vol. 77, N.º 2 (2015), pp. 377-390pt_PT
dc.identifier.doi10.1007/s10846-013-0016-3pt_PT
dc.identifier.issn0921-0296
dc.identifier.issn1573-0409
dc.identifier.urihttp://hdl.handle.net/10400.21/6035
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.ispartofseriesSI;
dc.subjectRobot visual localizationpt_PT
dc.subjectLocal image featurespt_PT
dc.subjectInformation fusionpt_PT
dc.subjectMultiple classifier systemspt_PT
dc.subjectDiscriminativitypt_PT
dc.titleRobot visual localization through local feature fusion: an evaluation of multiple classifiers combination approachespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage390pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage377pt_PT
oaire.citation.titleJournal of Intelligent & Robotics Systemspt_PT
oaire.citation.volume77pt_PT
person.familyNameMarnoto de Oliveira Campos
person.familyNameCalado
person.givenNameFrancisco Mateus
person.givenNameJoão
person.identifier370725
person.identifier.ciencia-idCD12-E777-0C7F
person.identifier.ciencia-idB518-93E3-E7AB
person.identifier.orcid0000-0002-1481-0042
person.identifier.orcid0000-0001-6628-4657
person.identifier.ridM-4167-2013
person.identifier.scopus-author-id7006897277
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication7fd2ee52-b866-48ca-ab91-e194ca054ae2
relation.isAuthorOfPublication602b1546-f4f1-4cd5-8d29-d835d54c9bd6
relation.isAuthorOfPublication.latestForDiscovery7fd2ee52-b866-48ca-ab91-e194ca054ae2

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