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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allan, D.W.: Statistics of atomic frequency standards. Proceedings of the IEEE 54(2), 221–230 (1966)CrossRefGoogle Scholar
  2. 2.
    Badino, H.: Binocular ego-motion estimation for automotive applications. PhD thesis. Frankfurt/Main University (2008)Google Scholar
  3. 3.
    Calvetti, D.: A stochastic roundoff error analysis for the convolution. Mathematics of Computation 59, 569–582 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cheng, Y., Maimone, M.W., Matthies, L.: Visual odometry on the Mars exploration rovers. IEEE Robotics Automation Magazine 13(2), 54–62 (2006)CrossRefGoogle Scholar
  5. 5.
    Comport, A.I., Malis, E., Rives, P.: Real-time quadrifocal visual odometry. Int. J. Robotics Research 29, 245–266 (2010)CrossRefGoogle Scholar
  6. 6.
    Corke, P., Detwiler, C., Dunbabin, M., Hamilton, M., Rus, D., Vasilescu, L.: Experiments with underwater robot localization and tracking. In: IEEE Int. Conf. Robotics Automation, pp. 4556–4561 (2007)Google Scholar
  7. 7.
    Flenniken, W.S.: Modeling inertial measurement units and analyzing the effect of their errors in navigation applications. Master thesis, University of Auburn (2005)Google Scholar
  8. 8.
    IEEE standard specification format guide and test procedure for single-axis laser gyros. IEEE Std 647TM − 2006 (2006)Google Scholar
  9. 9.
    Kelly, A.: Linearized error propagation in odometry. The Int. J. of Robotics Research 23, 179–218 (2004)CrossRefGoogle Scholar
  10. 10.
    Kelly, J., Sukhatme, G.S.: An experimental study of aerial stereo visual odometry. In: IFAC Symp. Intelligent Autonomous Vehicles (2007)Google Scholar
  11. 11.
    Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry. In: IEEE Conf. Computer Vision Pattern Recognition, vol. 1, pp. 652–659 (2004)Google Scholar
  12. 12.
    Olson, C.F., Matthies, L.H., Schoppers, M., Maimone, M.W.: Stereo ego-motion improvements for robust rover navigation. In: IEEE Int. Conf. Robotics Automation, pp. 1099–1104 (2001)Google Scholar
  13. 13.
    Scaramuzza, D., Siegwart, R.: Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Trans. Robotics 24, 1015–1026 (2008)CrossRefGoogle Scholar
  14. 14.
    Sünderhauf, N., Protzel, P.: Towards using sparse bundle adjustment for robust stereo odometry in outdoor terrain. Towards Autonomous Robotic Systems, 206–213 (2006)Google Scholar
  15. 15.
    Wall, J.H., Bevly, D.M.: Characterization of inertial sensor measurements for navigation performance analysis. In: Proc. of ION GNSS, Long Beach, CA (2005)Google Scholar

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

Personalised recommendations