The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지

Localization Error Recovery Based on Bias Estimation

바이어스추정을 기반으로 한 위치추정의 오차회복

  • Received : 2009.02.17
  • Accepted : 2009.05.25
  • Published : 2009.05.29
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Abstract

In this paper, a localization error recoverymethod based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

Keywords

References

  1. A. Adam, E. Rivlin, and H. Rotstein, "Fusion of fixation and odometry for vehicle navigation," IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 29, No. 6, pp. 593-603, 1999. https://doi.org/10.1109/3468.798063
  2. Y. Bar-Shalom, X. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley & Sons, INC, New York, 2001.
  3. P. Bonnifait, P. Bouron, P. Crubille, and D. Meizel, "Data fusion of four ABS sensors and GPS for an enhanced localization of car-like vehicles," Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea, May 21-26, pp. 1597-1602, 2001.
  4. S. B. H. Bruder, "An information centric approach to heterogeneous multi-sensor integration for robotic applications," Robotics and Autonomous Systems, Vol. 26, No. 4, pp. 255-280, March 1999. https://doi.org/10.1016/S0921-8890(98)00057-8
  5. R. Carelli and E. O. Freire, "Corridor navigation and wall-following stable control for sonar-based mobile robots," Robotics and Autonomous Systems, Vol. 45, No. 3-4, pp. 235-247, December 2003. https://doi.org/10.1016/j.robot.2003.09.005
  6. N. A. Carlson and M. P. Berarducci, "Federated Kalman filter simulation results," Journal of the Institute of Navigation, Vol. 41, No. 3, pp. 297-321, 1994. https://doi.org/10.1002/j.2161-4296.1994.tb01882.x
  7. N. A. Carlson, "Federated square root filter for decentralized parallel processes," IEEE Transactions on Aerospace and Electronic Systems, Vol. 26, No. 3, pp. 517-525, 1990. https://doi.org/10.1109/7.106130
  8. F. Caron, E. Duflos, D. Pomorski, and Ph. Vanheeghe,"GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects," Information Fusion, 2005.
  9. M. P. Dana, "Registration: a prerequisite for multiple sensor tracking," in Multitarget-Multisensor Tracking: Advanced Applications, Y. Bar-Shalom, Ed., Artech House, Norwood, Ma, 1990.
  10. B. Friedland, "Treatment of bias in recursive filtering," IEEE Transactions on Automatic Control, vol. 14, pp. 359-367, 1969. https://doi.org/10.1109/TAC.1969.1099223
  11. J. Guivant, E. Nebot, and S. Baiker, "Localization and map building using laser sensors in outdoor applications," Journal of Robotic Systems, Vol. 17, No. 10, pp. 565-583, 2000. https://doi.org/10.1002/1097-4563(200010)17:10<565::AID-ROB4>3.0.CO;2-6
  12. J. Huang and H. S. Tan, "A low-order DGPS-based vehicle positioning system under urban environment," IEEE Transactions on Mechatronics, vol. 11, no. 5, pp. 567-575, 2006. https://doi.org/10.1109/TMECH.2006.882988
  13. T. G. Lee, "Centralized Kalman filter with adaptive measurement fusion: its application to a GPS/SDINS integration system with an additional sensor," International Journal of Control, Automation, and systems, vol. 1, no. 4, pp. 444-452, December 2003.
  14. J. H. Lim and C. U. Kang, "Grid-based Localization of a mobile robot using sonar sensors," KSME International Journal, Vol. 16, No. 3, pp. 302-309, 2002. https://doi.org/10.1007/BF03185227
  15. R. Madhavan and H. F. Durrant-Whyte, "Natural landmark-based autonomous vehicle navigation," Robotics and Autonomous Systems, Vol. 46, pp. 79-95, 2004. https://doi.org/10.1016/j.robot.2003.11.003
  16. E. M. Nebot and H. Durrant-Whyte, "A high integrity navigation architecture for outdoor autonomous vehicles," Robotics and Autonomous Systems, Vol. 26, No. 2-3, pp. 81-97, February 1999. https://doi.org/10.1016/S0921-8890(98)00062-1
  17. R. Y. Novoselov, S. M. Herman, S. M. Gadaleta, and A. B. Poore, "Mitigating the effects of residual biases with Schmidt-Kalman filtering," International Conference on Information Fusion, Philadelphia, PA, USA, July 25-29, 2005.
  18. K. Ohno, "A study on outdoor navigation of an autonomous mobile robot based on GPS," Ph.D. dissertation, Univ. of Tsukuba, Tsukuba, Japan, Mar. 2004.
  19. K. Ohno, T. Tsubouchi, B. Shigematsu, S. Maeyama, and S. Yuta, "Outdoor navigation of a mobile between buildings based on DGPS and odometry data fusion," Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan, Sep. 14-19, pp. 1978-1984, 2003.
  20. R. Thrapp, C. Westbrook, and D. Subramanian, "Robust localization algorithm for an autonomous campus tour guide," Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea, May. 21-26, pp. 2065-2071, 2001.
  21. Y. Zhou, H. Leung, and E. Bosse, "Registration of mobile sensors using the parallelized extended Kalman filter," Opt. Eng. vol. 36, no. 3, pp. 780-788, March, 1997. https://doi.org/10.1117/1.601275
  22. Y. Zhu, Z. You, J. Zhao, K. Zhang, and X. Li, "The optimality for the distributed Kalman filtering fusion," Automatica, Vol. 37, No. 9, pp. 1489-1493, 2001. https://doi.org/10.1016/S0005-1098(01)00074-7