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Global localization with non-quantized local image features

2012-08Journal article Restricted access
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dc.contributor.authorCampos, Francisco M.
dc.contributor.authorCorreia, Luís
dc.contributor.authorCalado, João Manuel Ferreira
dc.date.accessioned2015-09-07T09:42:56Z
dc.date.available2015-09-07T09:42:56Z
dc.date.issued2012-08
dc.description.abstractIn the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.por
dc.identifier.citationCAMPOS, F. M.; CORREIA, L.; CALADO, J. M. F. – Global localization with non-quantized local image features. Robotics and Autonomous Systems. ISSN: 0921-8890. Vol. 60, nr. 8 (2012), pp. 1011-1020por
dc.identifier.doi10.1016/j.robot.2012.05.015
dc.identifier.issn0921-8890
dc.identifier.urihttp://hdl.handle.net/10400.21/5068
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevier Science BVpor
dc.subjectTopological Localizationpor
dc.subjectAppearance-Based Methodspor
dc.subjectFeature Selectionpor
dc.subjectInformation Contentpor
dc.subjectEntropypor
dc.subjectTexture Classification
dc.subjectOmnidirectional Images
dc.subjectMarkov Localization
dc.subjectRobot Localization
dc.subjectBinary Patterns
dc.subjectMobile Robots
dc.subjectRecognition
dc.subjectAppearance
dc.titleGlobal localization with non-quantized local image featurespor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceAmsterdam
oaire.citation.endPage1020por
oaire.citation.issue8por
oaire.citation.startPage1011por
oaire.citation.titleRobotics and Autonomous Systemspor
oaire.citation.volume60por
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.rightsclosedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication7fd2ee52-b866-48ca-ab91-e194ca054ae2
relation.isAuthorOfPublication602b1546-f4f1-4cd5-8d29-d835d54c9bd6
relation.isAuthorOfPublication.latestForDiscovery7fd2ee52-b866-48ca-ab91-e194ca054ae2

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