Vehicle Localization Using Omnidirectional Camera with GPS Supporting in Wide Urban Area

  • My-Ha Le
  • Van-Dung Hoang
  • Andrey Vavilin
  • Kang-Hyun Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)


This paper proposes a method for long-range vehicle localization using fusion of omnidirectional camera and Global Positioning System (GPS) in wide urban environments. The main contributions are twofold: first, the positions estimated by visual sensor overcome the motion blur effects. The motion constrains of successive frames are obtained accurately under various scene structures and conditions. Second, the cumulative errors of visual odometry system are solved completely based on the fusion of local (visual odometry) and global positioning system. The visual odometry can yield the correct local position at short distance of movements but it will accumulate errors overtime, on the contrary, the GPS can yields the correct global positions but the local positions may be drifted. Moreover, the signals received from satellites are affected by multi-path and forward diffraction then the position errors increase when vehicles move in dense building regions or jump/miss in tunnels. To utilize the advantages of two sensors, the position information should be evaluated before fusion by Extended Kalman Filter (EKF) framework. This multiple sensor system can also compensate each other in the case of losing one of two. The simulation results demonstrate the accuracy of vehicle positions in long-range movements.


Omni-directional camera chamfer matching visual odometry GPS cumulative error EKF 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nistér, D., Naroditsky, O., Bergen, J.: Visual odometer. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 652–659 (2004)Google Scholar
  2. 2.
    Royer, E., Lhuillier, M., Dhome, M., Lavest, J.M.: Monocular vision for mobile robot localization and autonomous navigation. International Journal of Computer Vision 74, 237–260 (2007)CrossRefGoogle Scholar
  3. 3.
    Garca-Garca, R., Sotelo, M.A., Parra, I., Fernndez, D., Naranjo, J.E., Gaviln, M.: 3D visual odometry for road vehicles. Journal of Intelligent and Robotic Systems 51, 113–134 (2008)CrossRefGoogle Scholar
  4. 4.
    Konolige, K., Agrawal, M., Sol, J.: Large-scale visual odometry for rough terrain. In: Proceedings of the International Symposium on Robotics Research (2007)Google Scholar
  5. 5.
    Scaramuzza, D., Fraundorfer, F., Siegwart, R.: Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC. In: IEEE International Conference on Robotics and Automation, pp. 4293–4299 (2009)Google Scholar
  6. 6.
    Scaramuzza, D., Siegwart, R.: Appearance guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Transactions on Robotics 24, 1015–1026 (2008)CrossRefGoogle Scholar
  7. 7.
    Gandhi, T., Trivedi, M.: Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera. Machine Vision and Applications 16, 85–95 (2005)CrossRefGoogle Scholar
  8. 8.
    Tardif, J.P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidirectional camera. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2531–2538 (2008)Google Scholar
  9. 9.
    El Najjar, M.E., Bonnifait, P.: A road-matching method for precise vehicle localization using Belief Theory and Kalman filtering. Autonomous Robots 19, 173–191 (2005)CrossRefGoogle Scholar
  10. 10.
    Sukkarieh, S., Nebot, E.M., Durrant-Whyte, H.F.: A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Transactions on Robotics and Automation 15, 572–578 (1999)CrossRefGoogle Scholar
  11. 11.
    Cappelle, C., El Badaoui El Najjar, M., Pomorski, D., Charpillet, F.: Intelligent geolocalization in urban areas using global positioning systems, three-dimensional geographic information systems, and vision. Journal of Intelligent Transportation Systems 14, 3–12 (2010)zbMATHCrossRefGoogle Scholar
  12. 12.
    Grimes, M., LeCun, Y.: Efficient off-road localization using visually corrected odometry. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2649–2654 (2009)Google Scholar
  13. 13.
    Wei, L., Cappelle, C., Ruichek, Y., Zann, F.: GPS and Stereovision-Based Visual Odometry: Application to Urban Scene Mapping and Intelligent Vehicle Localization. International Journal of Vehicular Technology (2011)Google Scholar
  14. 14.
    Stella, E., Cicirelli, G., Lovergine, F.P., Distante, A.: Position estimation for a mobile robot using data fusion. Intelligent Control, 565–570 (1995)Google Scholar
  15. 15.
    Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: two new techniques for image matching. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 659–663 (1977)Google Scholar
  16. 16.
    Ohno, K., Tsubouchi, T., Shigematsu, B., Yuta, S.: Differential GPS and odometry-based outdoor navigation of a mobile robot. Advanced Robotics 6, 611–635 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • My-Ha Le
    • 1
  • Van-Dung Hoang
    • 1
  • Andrey Vavilin
    • 1
  • Kang-Hyun Jo
    • 1
  1. 1.Graduated School of Electrical EngineeringUniversity of UlsanUlsanKorea

Personalised recommendations