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Learning a Confidence Measure for Real-Time Egomotion Estimation

  • Stephanie LessmannEmail author
  • Jens Westerhoff
  • Mirko Meuter
  • Josef Pauli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

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.

Keywords

Kalman Filter Optical Flow Support Vector Regression System Availability Confidence Measurement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Stephanie Lessmann
    • 1
    • 3
    Email author
  • Jens Westerhoff
    • 1
    • 2
  • Mirko Meuter
    • 1
  • Josef Pauli
    • 3
  1. 1.Delphi Electronics and SafetyWuppertalGermany
  2. 2.University of WuppertalWuppertalGermany
  3. 3.University of Duisburg-EssenDuisburgGermany

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