Probabilistic Egomotion for Stereo Visual Odometry

dc.contributor.author Hugo Miguel Silva en
dc.contributor.author Bernardino,A en
dc.contributor.author Eduardo Silva en
dc.date.accessioned 2018-01-10T15:27:01Z
dc.date.available 2018-01-10T15:27:01Z
dc.date.issued 2015 en
dc.description.abstract We 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.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5882
dc.identifier.uri http://dx.doi.org/10.1007/s10846-014-0054-5 en
dc.language eng en
dc.relation 5429 en
dc.relation 5570 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Probabilistic Egomotion for Stereo Visual Odometry en
dc.type article en
dc.type Publication en
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