Cited By
View all- Sardana RKarar VPoddar S(2023)Improving visual odometry pipeline with feedback from forward and backward motion estimatesMachine Vision and Applications10.1007/s00138-023-01370-w34:2Online publication date: 27-Jan-2023
Probabilistic Egomotion for Stereo Visual Odometry
Authors: 
H. Silva, A. Bernardino, E. SilvaAuthors Info & Claims
Pages 265 - 280
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.
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- Probabilistic Egomotion for Stereo Visual Odometry
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Published: 01 February 2015
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View all- Sardana RKarar VPoddar S(2023)Improving visual odometry pipeline with feedback from forward and backward motion estimatesMachine Vision and Applications10.1007/s00138-023-01370-w34:2Online publication date: 27-Jan-2023