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Learning a Confidence Measure for Real-Time Egomotion Estimation

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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

This paper presents a method to generate a meaningful confidence measurement during online real-time egomotion estimation of a vehicle using a monocular camera. This confidence measurement should give the information whether the signal fulfills a certain accuracy range in all parameters or not. For that reason features from an optical flow field incorporating the egomotion error are determined and a confidence measurement is learned using ground truth egomotion data that we obtain from an offline bundle adjustment before. We show that our confidence measurement gives reliable results and can further be used to filter the egomotion estimation using a Kalman filter. Incorporating the knowledge of the egomotion accuracy determined by the confidence we are able to update the confidence measure for the filtered results. This leads to an improved system availability.

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Authors and Affiliations

  1. Delphi Electronics and Safety, Wuppertal, Germany

    Stephanie Lessmann, Jens Westerhoff & Mirko Meuter

  2. University of Wuppertal, Wuppertal, Germany

    Jens Westerhoff

  3. University of Duisburg-Essen, Duisburg, Germany

    Stephanie Lessmann & Josef Pauli

Authors
  1. Stephanie Lessmann
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  2. Jens Westerhoff
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  3. Mirko Meuter
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  4. Josef Pauli
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Corresponding author

Correspondence to Stephanie Lessmann .

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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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Lessmann, S., Westerhoff, J., Meuter, M., Pauli, J. (2016). Learning a Confidence Measure for Real-Time Egomotion Estimation. 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_32

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_32

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  • Print ISBN: 978-3-319-45885-4

  • Online ISBN: 978-3-319-45886-1

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