Accurate Vehicle Positioning on a Numerical Map

  • Published : 2005.03.01

Abstract

Nowadays, the road safety is an important research field. One of the principal research topics in this field is the vehicle localization in the road network. This article presents an approach of multi sensor fusion able to locate a vehicle with a decimeter precision. The different informations used in this method come from the following sensors: a low cost GPS, a numeric camera, an odometer and a steer angle sensor. Taking into account a complete model of errors on GPS data (bias on position and nonwhite errors) as well as the data provided by an original approach coupling a vision algorithm with a precise numerical map allow us to get this precision.

Keywords

References

  1. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, 'A Tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking,' IEEE Trans. on Signal Processing, vol. 50, no. 2, pp. 174-188, February 2002 https://doi.org/10.1109/78.978374
  2. R. Aufrere, Reconnaissance et suivi de route par vision artificielle application a l'aide a la conduite, PhD thesis, Universite Blaise Pascal, Clermont-Ferrand, France, 2001
  3. P. Bonnifait, Localisation preise en position et attitude des robots mobiles d'exteieur, PhD thesis, Ecole centrale de Nantes, France, 1997
  4. J. Borenstein and L.Feng, 'Gyrodometry: A new method for combining data from gyros and odometry in mobile robots,' Proc. of the IEEE International Conference on Robotics and Automation, pp. 423-428, Minneapolis, Minnesota, April 22-28, 1996
  5. S. Botton, F. Duquenne, Y. Egels, M. Even, and P. Willis, GPS Localisation et Navigation, Hermes, 1998
  6. D. Bouvet, Contribution a la localisation d'engins de chantiers routiers, PhD thesis, Ecole centrale de Nantes, France, 2000
  7. R. Chapuis, J. Laneurit, R.Aufrere, F. Chausse, and T. Chateau, 'Accurate vision based road tracker,' Proc. of IEEE Int. Conf. on Intelligent Vehicles, Versailles, France, CD-ROM file no IV-103.pdf, June 18-20, 2002
  8. M. Chung, L. Ojeda, and J. Borenstein, 'Sensor fusion for mobile robot dead-reckoning with a precision-calibrated fiber optic gyroscope,' Proc. of the IEEE International Conference on Robotics and Automation, pp. 3588-3593, Korea, May 21-26, 2001
  9. S. Clark, G. Dissanayake, P. Newman, and H. Durrant-White, 'A solution to simultaneous localization and map building (SLAM) problem,' International Journal of Robotic and Automation, vol. 17, no. 3, pp. 229-241, 2001 https://doi.org/10.1109/70.938381
  10. P. H. Dana, 'Global positioning system (GPS) time dissemination for real-time applications,' International Journal of Time Critical Computing Systems, vol. 12, no. 1, pp. 9-40, 1997
  11. C. Durieu, M. J. Aldon, and D. Meizel, 'La fusion de donnees multisensorielles pour la localisation en robotique mobile,' Traitement du signal, vol. 13, no. 2, pp. 143-166, 1996
  12. E. Kiriy and M. Buehler, Three-state Extended Kalman Filter for Mobile Robot Localization, Technical Report, Electrical and Computer Engineering, McGill University, Montreal, 2002
  13. M. E. El Najjar and Ph. Bonnifait, 'A roadmap matching method for precise vehicle localization using belief theory and Kalman filtering,' Proc. of International Conference on Advanced Robotics, pp. 1677-1682, Portugal, July 2003
  14. K. Ohno, T. Tsubouchi, B. Shigematsu, S. Maeyama, and S. Yuta, 'Outdoor navigation of a mobile robot between buildings based on DGPS and odometry data fusion,' Proc. of IEEE Int'l Conf. on Robotics and Automation, pp. 1978- 1984, September 14-15, 2003
  15. C. F. Olson, 'Selecting landmarks for localization in natural terrain,' Autonomous Robots Systems, vol. 12, no. 2, pp. 201-210, March 2002
  16. L. Pronzato and E. Walte, 'Minimal-volume ellipsoids,' International Journal of Adaptive Control and Signal Processing, vol. 8, no. 2, pp. 15-30, 1994 https://doi.org/10.1002/acs.4480080103
  17. I. M. Rekleitis, Cooperative Localization and Multi-Robot Exploration, School of Computer Science, McGill University, Montreal, Quebec, Canada, 2003
  18. U. Scheunert, H. Cramer, and G. Wanielik, 'Precise vehicle localization using multiple sensors and natural landmarks,' Proc. of the Seventh International Conference on Information Fusion, pp. 649-656, Stockholm, Sweden, June, 2004
  19. F. C. Schweppe, 'Recursive state estimation : unknown but bounded errors and system inputs,' IEEE Trans. on Automatic Control, vol. 13, no. 1, pp. 22-28, 1968 https://doi.org/10.1109/TAC.1968.1098790
  20. R. Talluri and J. K. Aggarwal, 'Image/map correspondence for mobile robot self-location using computer graphics,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 6, pp. 597-601, June 1993 https://doi.org/10.1109/34.216729
  21. R. Thrapp, C. Westbrook, and S. Devika, 'Robust localization methods for an autonomous campus tour guide,' Proc. of the International Conference on Robotics and Automation, Korea, May 21-26, 2001
  22. S. Thrun, D. Fox, W. Burgard, and F. Dellaert, 'Robust monte carlo localization for mobile robots,' Artificial Intelligence, vol. 128, no. 1-2, pp. 99-141, 2000 https://doi.org/10.1016/S0004-3702(01)00069-8
  23. J. Vaganay, J. G. Belligham, and J. Leonard, 'Comparison of fix computation and filtering for autonomous acoustic navigation,' International Journal of Systems Science, vol. 29, pp. 1111- 1122, 1998 https://doi.org/10.1080/00207729808929601
  24. J. Vaganay, Conception d'un systeme multisensoriel de localisation dynamique 3D pour robot mobile, PhD thesis, University Montpellier II, France, 1993
  25. G. Welch and G. Bishop, An Introduction to the Kalman Filter, University of North Carolina, Department of Computer Science, Chapel Hill, NC, USA, TR95-041, 2004
  26. Y. Cui and S. S. Ge, 'Autonomous vehicle positioning with GPS in urban canyon environments,' IEEE Trans. on Robotics and Automation, vol. 19, no. 1, pp. 15-25, February 2003 https://doi.org/10.1109/TRA.2002.807557