The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지

Strategic Games for Particle Survival in Rao-Blackwellized Particle Filter for SLAM

Rao-Blackwellized 파티클 필터에서 파티클 생존을 위한 전략 게임

  • 곽노산 (일본 산업총합기술연구원(AIST)) ;
  • ;
  • Received : 2009.02.12
  • Accepted : 2009.04.06
  • Published : 2009.05.29
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, due to the usage of the accurate sensors, distinct particles which compensate one another are attenuated as the RBPF-SLAM continues. To avoid this particle depletion, we propose the strategic games to assign the particle's payoff which replaces the importance weight in the current RBPF-SLAM framework. From simulation works, we show that RBPF-SLAM with the strategic games is inconsistent in the pessimistic way, which is different from the existing optimistic RBPF-SLAM. In addition, first, the estimation errors with applying the strategic games are much less than those of the standard RBPF-SLAM, and second, the particle depletion is alleviated.

Keywords

References

  1. K. Murphy, "Bayesian map learning in dynamic environments," Advanced in Neural Information Processing Systems, MIT Press, 1999.
  2. A. Doucet, N. de Freitas, K. Murphy, et al., "Rao-Blackwellized particle filtering for dynamic Bayesian networks," in Proc. of Conference on Uncertainty in Artificial Intelligence, pp. 176-183, 2000.
  3. M. Montemerlo, FastSLAM: A factored solution to the simultaneous localization and mapping problem with unknown data association, Ph.D. Dissertation, Carnegie Mellon University, 2003.
  4. R. Merwe, A. Doucet, N. de Freitas, et al., The unscented particle filter, Cambridge University Engineering Department, Technical Report CUED/F INFENG/TR380, 2000.
  5. T. Bailey, J. Nieto, and E. Nebot, "Consistency of the FastSLAM algorithm," in Proc. of IEEE Int'l Conf. on Robotics and Automation, pp. 424-429, 2006.
  6. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley and Sons, 2001.
  7. K. R. Beevers and Wesley H. Huang, "Fixed-lag sampling strategies for particle filtering SLAM," in Proc. of IEEE International Conference on Robotics and Automation, pp. 2433-2438, Rome, April 2007
  8. C. Kim, R. Sakthivel, and W. K. Chung, "Unscented FastSLAM: A robust and efficient solution to the SLAM problem," IEEE Trans. on Robotics, Vol. 24, No. 4, Aug., pp. 808-820, 2008. https://doi.org/10.1109/TRO.2008.924946
  9. C. Kim and W. K. Chung, "Delayed Resampling in aRao-Blackwellized Particle Filtering for SLAM for Consistent Loop Closures," Proc. of 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2084-2090, 2008.
  10. N. Kwak, G. W. Kim, and B. H. Lee, "A new compensation technique based on analysis of resampling process in FastSLAM," Robotica, vol. 26, no. 2, pp. 205-217, March 2008.
  11. N. Kwak, B. H. Lee, and K. Yokoi, "Result Representation of Rao-Blackwellized Particle Filter for Mobile Robot SLAM," Journal of Korea Robotics Society, vol. 3, no. 4, pp. 308-314, December 2008.
  12. S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics, Cambridge: MIT Press, 2005.
  13. J.-K. Choi and S. Bowles, "The coevolution of parochial altruism and war," Science, vol. 318, pp. 636-640, 2007. https://doi.org/10.1126/science.1144237
  14. A. Dixit and S. Skeath, Games of Strategy 2nd Edition, W. W. Norton & Company, Inc., 2004.
  15. J. S. Liu and R. Chen, "Blind deconvolution via sequential imputations,"Journal of the American Statistical Association, vol. 90, no. 430, pp. 567-576, 1995. https://doi.org/10.2307/2291068
  16. The Evolution of Cooperation, New York: Basic Books, 1984.