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6D Visual Odometry with Dense Probabilistic Egomotion Estimation

dc.contributor.authorSilva, Hugo Miguel
dc.contributor.authorBernardino, Alexandre
dc.contributor.authorSilva, Eduardo
dc.date.accessioned2015-12-29T10:47:35Z
dc.date.available2015-12-29T10:47:35Z
dc.date.issued2013
dc.descriptionProceedings of the International Conference on Computer Vision Theory and Applications, 361-365, 2013, Barcelona, Spainpt_PT
dc.description.abstractWe present a novel approach to 6D visual odometry for vehicles with calibrated stereo cameras. A dense probabilistic egomotion (5D) method is combined with robust stereo feature based approaches and Extended Kalman Filtering (EKF) techniques to provide high quality estimates of vehicle’s angular and linear velocities. Experimental results show that the proposed method compares favorably with state-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.pt_PT
dc.identifier.doi10.5220/0004196103610365pt_PT
dc.identifier.isbn978-989-8565-48-8
dc.identifier.urihttp://hdl.handle.net/10400.22/7284
dc.language.isoengpt_PT
dc.publisherSCITEPRESS Digital Librarypt_PT
dc.relation.ispartofseriesVISAPP2013;Vol. 2
dc.relation.publisherversionhttp://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0004196103610365pt_PT
dc.subjectVisual Navigationpt_PT
dc.subjectStereo Vision,pt_PT
dc.subjectVisual Odometrypt_PT
dc.subjectEgomotionpt_PT
dc.title6D Visual Odometry with Dense Probabilistic Egomotion Estimationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceBarcelona, Espanhapt_PT
oaire.citation.endPage365pt_PT
oaire.citation.startPage361pt_PT
oaire.citation.titleVISAPP2013 - 8th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applicationspt_PT
person.familyNameSilva
person.givenNameEduardo
person.identifier.ciencia-idC517-23DA-B09F
person.identifier.orcid0000-0001-7166-3459
person.identifier.ridM-7929-2014
person.identifier.scopus-author-id6507130721
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd0912771-16c3-4f41-a936-79a714e984fb
relation.isAuthorOfPublication.latestForDiscoveryd0912771-16c3-4f41-a936-79a714e984fb

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