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Probabilistic Egomotion for Stereo Visual Odometry

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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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Authors and Affiliations

  1. INESC TEC (formerly INESC Porto), Instituto Superior de Engenharia do Porto, Porto, Portugal

    H. Silva & E. Silva

  2. Institute for Systems and Robotics, Instituto Superior Técnico (IST) Lisbon, Lisbon, Portugal

    A. Bernardino

Authors
  1. H. Silva
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  2. A. Bernardino
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  3. E. Silva
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Correspondence to H. Silva.

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Silva, H., Bernardino, A. & Silva, E. Probabilistic Egomotion for Stereo Visual Odometry. J Intell Robot Syst 77, 265–280 (2015). https://doi.org/10.1007/s10846-014-0054-5

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  • DOI: https://doi.org/10.1007/s10846-014-0054-5

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