Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5882
Title: Probabilistic Egomotion for Stereo Visual Odometry
Authors: Hugo Miguel Silva
Bernardino,A
Eduardo Silva
Issue Date: 2015
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.
URI: http://repositorio.inesctec.pt/handle/123456789/5882
http://dx.doi.org/10.1007/s10846-014-0054-5
metadata.dc.type: article
Publication
Appears in Collections:CRAS - Articles in International Journals

Files in This Item:
File Description SizeFormat 
P-00A-3JM.pdf6.87 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.