[1]房芳,马旭东,戴先中.一种新的移动机器人Monte Carlo自主定位算法[J].东南大学学报(自然科学版),2007,37(1):40-44.[doi:10.3969/j.issn.1001-0505.2007.01.010] 　Fang Fang,Ma Xudong,Dai Xianzhong.New Monte Carlo algorithm for mobile robot self-localization[J].Journal of Southeast University (Natural Science Edition),2007,37(1):40-44.[doi:10.3969/j.issn.1001-0505.2007.01.010] 点击复制 一种新的移动机器人Monte Carlo自主定位算法() 分享到： var jiathis_config = { data_track_clickback: true };

37

2007年第1期

40-44

2007-01-20

文章信息/Info

Title:
New Monte Carlo algorithm for mobile robot self-localization

Author(s):
School of Automatic, Southeast University, Nanjing 210096, China

Keywords:

TP24
DOI:
10.3969/j.issn.1001-0505.2007.01.010

Abstract:
A novel Monte Carlo method is proposed aiming at the solution of unmodeled motion problem(such as bumping or kidnapped problem)which is inextricable merely using conventional Monte Carlo localization. By adopting both p(Xk〖JB<1|〗zk) and p(Xk〖JB<1|〗Xk-1)1 as importance functions and sampling from them, the global localization and kidnapped problems are figured out efficiently. The over-convergence and uniformity validations are introduced to verify correspondence between sample distribution and sensor information for timely resampling which highly saves computational resource and enhances localization efficiency. Experimental results validate the favorable performance of this approach.

参考文献/References:

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[2] Fox D,Hightower J,Liao J,et al.Bayesian filtering for location estimation[J].IEEE Pervasive Computing,2003,2(3):24-33.
[3] Kabuka M R,Arenas A E.Position verification of a mobile robot using standard pattern[J].IEEE Journal of Robotics and Automation,1987,3(6):505-516.
[4] Leonard J,Durrant-Whyte H F.Mobile robot localization by tracking geometric beacons[J].IEEE Transactions on Robotics and Automation,1991,7(3):376-382.
[5] Simmons R,Koenig S.Probabilistic robot navigation in partially observable environments[C] //International Joint Conference on Artificial Intelligence.Montreal,Canada,1995:1080-1087.
[6] Fox D,Thrun S,Dellaert F,et al.Sequential MCL methods in practice particle:filters for mobile robot localization [M].New York:Springer-Verlag,2001:470-498.
[7] Fox D.A dapting the sample size in particle filter through KLD-Sampling[J].International Journal of Robotics Research,2003,22(12):985-1004.
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