Robust Visual Odometry Using Uncertainty Models

  • David Van Hamme
  • Peter Veelaert
  • Wilfried Philips
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)


In dense, urban environments, GPS by itself cannot be relied on to provide accurate positioning information. Signal reception issues (e.g. occlusion, multi-path effects) often prevent the GPS receiver from getting a positional lock, causing holes in the absolute positioning data. In order to keep assisting the driver, other sensors are required to track the vehicle motion during these periods of GPS disturbance. In this paper, we propose a novel method to use a single on-board consumer-grade camera to estimate the relative vehicle motion. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Preliminary testing shows good accuracy and reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance. The effects of inaccurate calibration are examined using artificial datasets, suggesting a self-calibrating system may be possible in future work.


Uncertainty Model Ground Plane Vehicle Motion Rotational Error World Coordinate System 
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.
    Amidi, O., Kanade, T., Miller, J.: Vision-based autonomus helicopter research at cmu. In: Proc. of Heli Japan 1998 (1998)Google Scholar
  2. 2.
    Azuma, T., Sugimoto, S., Okutomi, M.: Egomotion estimation using planar and non-planar constraints. In: Intelligent Vehicles Symposium (IV), pp. 855–862. IEEE, Los Alamitos (2010)Google Scholar
  3. 3.
    Bouguet, J.: Visual Methods for Three-Dimensional Modeling. Ph.D. thesis, California Institute of Technology (May 1999)Google Scholar
  4. 4.
    Campbell, J., Sukthankar, R., Nourbakhsh, I., Pahwa, A.: A robust visual odometry and precipice detection system using consumer-grade monocular vision. In: Proc. of IEEE Int. Conf on Robotics and Automation (ICRA) 2005, pp. 3421–3427 (2005)Google Scholar
  5. 5.
    Cheng, Y., Maimone, M., Matthies, L.: Visual odometry on the mars exploration rovers. IEEE Robotics and Automation Magazine 13(2) (2006)Google Scholar
  6. 6.
    Comport, A., Malis, E., Rives, P.: Accurate quadrifocal tracking for robust 3d visual odometry. In: Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA) 2007, pp. 40–45 (2007)Google Scholar
  7. 7.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kitt, B., Geiger, A., Lategahn, H.: Visual odometry based on stereo image sequences with ransac-based outlier rejection scheme. In: Intelligent Vehicles Symposium (IV), pp. 486–492. IEEE, Los Alamitos (2010)Google Scholar
  9. 9.
    Konolige, K., Agrawal, M., Sol, J.: Large-scale visual odometry for rough terrain. In: Int. Symposium on Research in Robotics (2007)Google Scholar
  10. 10.
    Levin, A., Szeliski, R.: Visual odometry and map correlation. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition 2004, vol. 1-I, pp. 611–618 (2004)Google Scholar
  11. 11.
    Marks, R., Wang, H., Lee, M., Rock, S.: Automatic visual station keeping of an underwater robot. In: Proc. of IEEE Oceans 1994, pp. 137–142 (1994)Google Scholar
  12. 12.
    Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., Sayd, P.: Generic and real-time structure from motion using local bundle adjustment. Image and Vision Computing 27(8) (2009)Google Scholar
  13. 13.
    Negahdaripour, S., Horn, B.: Direct passive navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(1) (1987)Google Scholar
  14. 14.
    Nistér, D.: An efficient solution to the five-point relative point problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6) (2004)Google Scholar
  15. 15.
    Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. Journal of Field Robotics 23 (2006)Google Scholar
  16. 16.
    Obdržálek, S., Matas, J.: A voting strategy for visual ego-motion from stereo. In: Intelligent Vehicles Symposium (IV), pp. 382–387. IEEE, Los Alamitos (2010)Google Scholar
  17. 17.
    Scaramuzza, D., Fraundorfer, F., Siegwart, R.: Real-time monocular visual odometry for on-road vehicles with 1-point ransac. In: Proc. of IEEE Int. Conf on Robotics and Automation (ICRA) 2009, pp. 4293–4299 (2009)Google Scholar
  18. 18.
    Tardif, J.-P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidrectional camera. In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) 2008, pp. 2531–2538 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • David Van Hamme
    • 1
    • 2
  • Peter Veelaert
    • 1
    • 2
  • Wilfried Philips
    • 2
  1. 1.Vision SystemsUniversity College GhentBelgium
  2. 2.IPIGhent University/IBBTBelgium

Personalised recommendations