Egomotion Estimation by Point-Cloud Back-Mapping

  • Haokun Geng
  • Radu Nicolescu
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8671)


We consider egomotion estimation in the context of driver-assistance systems. In order to estimate the actual vehicle movement we only apply stereo cameras (and not any additional sensor). The paper proposes a visual odometry method by back-mapping clouds of reconstructed 3D points. Our method, called stereo-vision point-cloud back mapping method (sPBM), aims at minimizing 3D back-projection errors. We report about extensive experiments for sPBM. At this stage we consider accuracy as being the first priority; optimizing run-time performance will need to be considered later. Accurately estimated motion among subsequent frames of a recorded video sequence can then be used, for example, for 3D roadside reconstruction.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haokun Geng
    • 1
  • Radu Nicolescu
    • 1
  • Reinhard Klette
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandNew Zealand

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