Advertisement

Visual odometry based on a Bernoulli filter

  • Feihu ZhangEmail author
  • Daniel Clarke
  • Alois Knoll
Special Section on Advanced Control Theory and Techniques based on Data Fusion

Abstract

In this paper, we propose a Bernoulli filter for estimating a vehicle’s trajectory under random finite set (RFS) framework. In contrast to other approaches, ego-motion vector is considered as the state of an extended target while the features are considered as multiple measurements that originated from the target. The Bernoulli filter estimates the state of the extended target instead of tracking individual features, which presents a recursive filtering framework in the presence of high association uncertainty. Experimental results illustrate that the proposed approach exhibits good robustness under real traffic scenarios.

Keywords

Bernoulli filter ego-motion vector random finite set 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D. Scaramuzza, F. Fraundorfer, M. Pollefeys, and R. Siegwart, “Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints,” Proc. of International Conference on Computer Vision, pp. 1413–1419, October 2009.Google Scholar
  2. [2]
    G. Garcia, M. A. Sotelo, I. Parra, D. Fernandez, and M. Gavilan, “2D visual odometry method for global positioning measurement,” Proc. of International Symposium on Intelligent Signal Processing, pp. 1–6, October 2007.Google Scholar
  3. [3]
    A. Davison, “Real-time simultaneous localization and mapping with a single camera,” Proc. of International Conference on Computer Vision, pp. 1403–1410, 2003.CrossRefGoogle Scholar
  4. [4]
    D. Burschka and G. D. Hager, “V-GPS (SLAM): vision-based inertial system for mobile robots,” Proc. of International Conference on Robotics and Automation, pp. 409–415, May 2004.Google Scholar
  5. [5]
    K. Konolige, M. Agrawal, and J. Sola, “Large-scale visual odometry for rough terrain,” Proc. of International Symposium on Research in Robotics, pp. 201–212, November 2007.Google Scholar
  6. [6]
    D. Scaramuzza and R. Siegwart, “Appearanceguided monocular omnidirectional visual odometry for outdoor ground vehicles,” IEEE Trans. on Robotics, vol. 24, no. 5, pp. 1015–1026, October 2008.CrossRefGoogle Scholar
  7. [7]
    D. Scaramuzza, F. Fraundorfer, and R. Siegwart, “Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC,” Proc. of International Conference on Robotics and Automation, pp. 4293–4299, May 2009.Google Scholar
  8. [8]
    C. McCarthy and N. Barnes, “Performance of optical flow techniques for indoor navigation with a mobile robot,” Proc. of International Conference on Robotics and Automation, pp. 5093–5098, May 2004.Google Scholar
  9. [9]
    J. Campbell, R. Sukthankar, and I. Nourbakhsh, “Techniques for evaluating optical flow for visual odometry in extreme terrain,” Proc. of International Conference on Intelligent Robots and Systems, pp. 3704–3711, October 2004.Google Scholar
  10. [10]
    P. Corke, D. Strelow, and S. Singh, “Omnidirectional visual odometry for a planetary rover,” Proc. of International Conference on Intelligent Robots and Systems, pp. 4007–4012, October 2004Google Scholar
  11. [11]
    F. Zhang, H. Stähle, A. Gaschler, C. Buckl, and A. Knoll, “Single camera visual odometry based on Random Finite Set Statistics,” Proc. of International Conference on Intelligent Robots and Systems, pp. 559–566, October 2012.Google Scholar
  12. [12]
    F. Zhang, H. Stähle, G. Chen, C. Buckl, and A. Knoll, “Visual odometry based on random finite set statistics in urban environment,” Proc. of Intelligent Vehicles Symposium, pp. 69–74, June 2012.Google Scholar
  13. [13]
    B.-T. Vo, C. See, N. Ma, and W. T. Ng, “Multisensor joint detection and tracking with the Bernoulli filter,” IEEE Trans. on Aerospace and Electronic Systems, vol. 48, no. 2, pp. 1385–1402, April 2012.CrossRefGoogle Scholar
  14. [14]
    B.-N. Vo and W.-K. Ma, “The Gaussian mixture probability hypothesis density filter,” IEEE Trans. on Signal Processing, vol. 54, no. 11, pp. 4091–4104, November 2006.CrossRefGoogle Scholar
  15. [15]
    W. Yang, Y. Fu, J. Long, and X. Li, “Joint detection, tracking, and classification of multiple targets in clutter using the PHD filter,” IEEE Trans. on Aerospace and Electronic Systems, vol. 48, no. 4, pp. 3594–3609, October 2012CrossRefGoogle Scholar
  16. [16]
    B. Ristic, B.-T. Vo, B.-N. Vo, and A. Farina, “A tutorial on Bernoulli filters: theory, implementation and applications,” IEEE Trans. on Signal Processing, vol. 61, no. 13, pp. 3406–3430, July 2013.CrossRefMathSciNetGoogle Scholar
  17. [17]
    M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. on Signal Processing, vol. 50, no. 2, pp. 174–188, February 2002.CrossRefGoogle Scholar
  18. [18]
    B. Kitt, A. Geiger, and H. Lategahn, “Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme,” Proc. of Intelligent Vehicles Symposium, pp. 486–492, June 2010.Google Scholar
  19. [19]
    C. Harris and M. J. Stephens, “A combined corner and edge detector,” Proc. of Alvey Vision Conference, pp. 147–152, 1988.Google Scholar
  20. [20]
    D. G. Lowe, “Object recognition from local scaleinvariant features,” Proc. of IEEE International Conference on Computer Vision, pp. 1150–1157, 1999.CrossRefGoogle Scholar
  21. [21]
    H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: speeded up robust features,” Proc. of European Conference on Computer Vision, pp. 404–417, May 2006.Google Scholar
  22. [22]
    C. Tomasi and T. Kanade, Detection and Tracking of Point Features, Carnegie Mellon, April 1991.Google Scholar
  23. [23]
    R. P. S. Mahler, “Multitarget Bayes filtering via first-order multitarget moments,” IEEE Trans. on Aerospace and Electronic Systems, no. 4, pp. 1152–1178, October 2003.CrossRefGoogle Scholar
  24. [24]
    N. Houshangi and F. Azizi, “Mobile robot position determination using data integration of odometry and gyroscope,” Proc. of Automation Congress, pp. 1–8, July 2006.Google Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Robotics and Embedded SystemsTechnische Universität MünchenGarching bei MünchenGermany
  2. 2.Cranfield UniversityDefense Academy of the United KindomShrivenham SN6 8LAUK

Personalised recommendations