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Combining sparse and dense methods in 6D Visual Odometry

2013Journal article Open access
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dc.contributor.authorSilva, Hugo Miguel
dc.contributor.authorSilva, Eduardo
dc.contributor.authorBernardino, Alexandre
dc.date.accessioned2015-12-29T11:11:06Z
dc.date.available2015-12-29T11:11:06Z
dc.date.issued2013
dc.description13th International Conference on Autonomous Robot Systems (Robotica), 2013, Lisboapt_PT
dc.description.abstractVisual Odometry is one of the most powerful, yet challenging, means of estimating robot ego-motion. By grounding perception to the static features in the environment, vision is able, in principle, to prevent the estimation bias rather common in other sensory modalities such as inertial measurement units or wheel odometers. We present a novel approach to ego-motion estimation of a mobile robot by using a 6D Visual Odometry Probabilistic Approach. Our approach exploits the complementarity of dense optical flow methods and sparse feature based methods to achieve 6D estimation of vehicle motion. A dense probabilistic method is used to robustly estimate the epipolar geometry between two consecutive stereo pairs; a sparse feature stereo approach to estimate feature depth; and an Absolute Orientation method like the Procrustes to estimate the global scale factor. We tested our proposed method on a known dataset and compared our 6D Visual Odometry Probabilistic Approach without filtering techniques against a implementation that uses the well known 5-point RANSAC algorithm. Moreover, comparison with an Inertial Measurement Unit (RTK-GPS) is also performed, for providing a more detailed evaluation of the method against ground-truth information.pt_PT
dc.identifier.doi10.1109/Robotica.2013.6623527pt_PT
dc.identifier.isbn978-1-4799-1246-9
dc.identifier.urihttp://hdl.handle.net/10400.22/7290
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.ispartofseriesRobótica;2013
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6623527&abstractAccess=no&userType=instpt_PT
dc.subject5-point RANSAC algorithmpt_PT
dc.subject6D visual odometry probabilistic approachpt_PT
dc.subjectProcrustes methodpt_PT
dc.subjectAbsolute orientation methodpt_PT
dc.subjectDense methodpt_PT
dc.subjectDense optical flow methodspt_PT
dc.titleCombining sparse and dense methods in 6D Visual Odometrypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceLisboapt_PT
oaire.citation.issue6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title13th International Conference on Autonomous Robot Systems (Robotica), 2013pt_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.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd0912771-16c3-4f41-a936-79a714e984fb
relation.isAuthorOfPublication.latestForDiscoveryd0912771-16c3-4f41-a936-79a714e984fb

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