Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5882
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dc.contributor.authorHugo Miguel Silvaen
dc.contributor.authorBernardino,Aen
dc.contributor.authorEduardo Silvaen
dc.date.accessioned2018-01-10T15:27:01Z-
dc.date.available2018-01-10T15:27:01Z-
dc.date.issued2015en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/5882-
dc.identifier.urihttp://dx.doi.org/10.1007/s10846-014-0054-5en
dc.description.abstractWe present a novel approach of Stereo Visual Odometry for vehicles equipped with calibrated stereo cameras. We combine a dense probabilistic 5D egomotion estimation method with a sparse keypoint based stereo approach to provide high quality estimates of vehicle's angular and linear velocities. To validate our approach, we perform two sets of experiments with a well known benchmarking dataset. First, we assess the quality of the raw velocity estimates in comparison to classical pose estimation algorithms. Second, we added to our method's instantaneous velocity estimates a Kalman Filter and compare its performance with a well known open source stereo Visual Odometry library. The presented results compare favorably with state-of-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.en
dc.languageengen
dc.relation5429en
dc.relation5570en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleProbabilistic Egomotion for Stereo Visual Odometryen
dc.typearticleen
dc.typePublicationen
Appears in Collections:CRAS - Articles in International Journals

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