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Probabilistic Egomotion for Stereo Visual Odometry
2014
Journal of Intelligent and Robotic Systems
We present a novel approach of probabilistic egomotion methods for Stereo Visual Odometry using 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
doi:10.1007/s10846-014-0054-5
fatcat:gsk5znjdsnflxnf7pgxdsstoge