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Stereo Visual Odometry Without Temporal Filtering

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

Visual Odometry is one of the key technology for navigating and perceiving the environment of an autonomous vehicle. Within the last ten years, a common sense has been established on how to implement high precision and robust systems. This paper goes one step back by avoiding temporal filtering and relying exclusively on pure measurements that have been carefully selected. The focus here is on estimating the ego-motion rather than a detailed reconstruction of the scene. Different approaches for selecting proper 3D-flows (scene flows) are compared and discussed. The ego-motion is computed by a standard P6P-approach encapsulated in a RANSAC environment. Finally, a slim method is proposed that is within the top ranks of the KITTI benchmark without using any filtering method like bundle adjustment or Kalman filtering.

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Author information

Authors and Affiliations

  1. Carl-Legien-Strasse 30, 63073, Offenbach am Main, Germany

    Joerg Deigmoeller & Julian Eggert

Authors
  1. Joerg Deigmoeller
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  2. Julian Eggert
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Corresponding author

Correspondence to Joerg Deigmoeller .

Editor information

Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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© 2016 Springer International Publishing AG

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Deigmoeller, J., Eggert, J. (2016). Stereo Visual Odometry Without Temporal Filtering. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45885-4

  • Online ISBN: 978-3-319-45886-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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