Rao-Blackwellized 파티클 필터에서 파티클 생존을 위한 전략 게임
Strategic Games for Particle Survival in Rao-Blackwellized Particle Filter for SLAM
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (16)
- K. Murphy, "Bayesian map learning in dynamic environments" (1999), Advanced in Neural Information Processing Systems
- A. Doucet, "Rao-Blackwellized particle filtering for dynamic Bayesian networks", pp.176~183 (2000), in Proc. of Conference on Uncertainty in Artificial Intelligence
- M. Montemerlo, "A factored solution to the simultaneous localization and mapping problem with unknown data association" (2003)
- The unscented particle filter, Cambridge University Engineering Department, "R. Merwe", Technical Report CUED, TR380, pp.2000, ~
- T. Bailey, "Consistency of the FastSLAM algorithm", in Proc. of IEEE Int’l Conf. on Robotics and Automation, pp.424~429 (2006)
- Y. Bar-Shalom, "Estimation with Applications to Tracking and Navigation" (2001), John Wiley and Sons
- K. R. Beevers, "Fixed-lag sampling strategies for particle filtering SLAM", pp.2433~2438 (2007), in Proc. of IEEE International Conference on Robotics and Automation
- C. Kim, "Unscented FastSLAM: A robust and efficient solution to the SLAM problem", IEEE Trans. on Robotics, 24`(4), pp.808~820 (2008)
- C. Kim, "Delayed Resampling in aRao-Blackwellized Particle Filtering for SLAM for Consistent Loop Closures", Proc. of 2008 IEEE, 2008, 2084~2090
- N. Kwak, "A new compensation technique based on analysis of resampling process in FastSLAM", Robotica, 26(2), pp.205~217 (2008)
- 곽노산, "Rao-Blackwellized 파티클 필터를 이용한 이동로봇의 위치 및 환경 인식 결과 도출", 로봇학회 논문지, 3(4), pp.308~314 (2008), 한국로봇학회
- S. Thrun, "Probabilistic robotics" (2005)
- J.-K. Choi, "The coevolution of parochial altruism and war", Science, 318, pp.636~640 (2007)
- A. Dixit, "Games of Strategy 2nd Edition" (2004), W. W. Norton & Company, Inc
- J. S. Liu, "Blind deconvolution via sequential imputations", Journal of the American Statistical Association, 90(430), pp.567~576 (1995)
- "The Evolution of Cooperation" (1984), Basic Books