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Statistical Modeling of Long-Range Drift in Visual Odometry

  • Ruyi Jiang
  • Reinhard Klette
  • Shigang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)

Abstract

An intrinsic problem of visual odometry is its drift in long-range navigation. The drift is caused by error accumulation, as visual odometry is based on relative measurements. The paper reviews algorithms that adopt various methods to minimize this drift. However, as far as we know, no work has been done to statistically model and analyze the intrinsic properties of this drift. Moreover, the quantification of drift using offset ratio has its drawbacks. This paper models the drift as a combination of wide-band noise and a first-order Gauss-Markov process, and analyzes it using Allan variance. The model’s parameters are identified by a statistical method. A novel drift quantification method using Monte Carlo simulation is also provided.

Keywords

Motion Vector Inertial Sensor Bundle Adjustment Allan Variance Visual Odometry 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ruyi Jiang
    • 1
  • Reinhard Klette
    • 2
  • Shigang Wang
    • 1
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.University of AucklandAucklandNew Zealand

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