[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]
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一种新的移动机器人Monte Carlo自主定位算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
37
期数:
2007年第1期
页码:
40-44
栏目:
自动化
出版日期:
2007-01-20

文章信息/Info

Title:
New Monte Carlo algorithm for mobile robot self-localization
作者:
房芳 马旭东 戴先中
东南大学自动化学院, 南京 210096
Author(s):
Fang Fang Ma Xudong Dai Xianzhong
School of Automatic, Southeast University, Nanjing 210096, China
关键词:
移动机器人 Monte Carlo算法 重要性函数 过收敛检验 均匀性检验
Keywords:
mobile robot Monte Carlo algorithm importance function over-convergence validation uniformity validation
分类号:
TP24
DOI:
10.3969/j.issn.1001-0505.2007.01.010
摘要:
针对当出现一些未建模的机器人运动时(如碰撞或者绑架问题),以小采样数目实现常规Monte Carlo方法难以解决的问题,提出一种新的Monte Carlo定位算法,该算法同时采用p(Xk〖JB<1|〗zk)p(Xk〖JB<1|〗Xk-1)1作为重要性函数并从中进行采样,避免了采样集不包含真实位姿采样的情况,能够有效地解决全局定位与绑架问题.同时在重采样过程中引入了过收敛检验与均匀性检验用于判断采样与感知信息的匹配程度,以适时进行重采样,节省了计算资源并提高了定位效率.实验结果表明该方法具有良好的性能.
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.
[8] Fox D.KLD-sampling:adaptive particle filters and mobile robot localization[R].Technical Report,TR-UW-CSE-01-08-02,2001.
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[10] Patric J,David A,Olle W,et al.Feature based condensation for mobile robot localization[C] //IEEE International Conference on Robotics and Automation.San Francisco,CA,2000:2531-2537.

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备注/Memo

备注/Memo:
基金项目: 国家重点基础研究发展计划(973计划)资助项目(2002CB312200)、国家高技术研究发展计划(863计划)资助项目(2005AA420060, 2004AA420110).
作者简介: 房芳(1980—),女,博士生; 马旭东(联系人),男,教授,博士生导师, xdma@seu.edu.cn.
更新日期/Last Update: 2007-01-20