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移动机器人的改进无迹粒子滤波蒙特卡罗定位算法 被引量：28

Mobile Robot Monte Carlo Localization Based on Improved Unscented Particle Filter
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摘要 粒子滤波是移动机器人蒙特卡罗定位(Monte Carlo localization,MCL)的核心环节.首先,针对粒子滤波过程的粒子退化问题,利用迭代Sigma点卡尔曼滤波来精确设计粒子滤波器的提议分布,以迭代更新方式将当前观测信息融入顺序重要性采样过程,提出IUPF(Improved unscented particle filter)算法.然后,将IUPF与移动机器人MCL相结合,给出IUPF-MCL定位算法的实现细节.仿真结果表明,IUPF-MCL是一种精确鲁棒的移动机器人定位算法. Particle filter is a key issue in mobile robot Monte Carlo location（MCL）.Firstly,improved unscented particle filter（IUPF） algorithm is proposed in this paper.To overcome particles degeneracy phenomenon,the algorithm utilizes iterated sigma points Kalman filter to generate more accurate proposal distribution,which introduces most recent measurement information into sequential importance sampling（SIS） routine through iterated update processing.Secondly,by applying IUPF to MCL,IUPF-MCL algorithm is given.Finally,simulation results show that IUPF-MCL is an accurate and robust mobile robot localization algorithm.
作者 宋宇 孙富春 李庆玲
机构地区 清华大学智能技术与系统国家重点实验室 哈尔滨工业大学机器人技术与系统国家重点实验室 中国科学院自动化研究所
出处 《自动化学报》 EI CSCD 北大核心 2010年第6期851-857,共7页 Acta Automatica Sinica
基金 国家高科术研究发展计划(863计划)(2007AA04Z232) 国家自然科学基金(60909055 90820304) 机器人技术与系统国家重点实验室开放研究项目(SKLRS-2009-ZD-04) 中国博士后科学基金(20080440382)资助~~
关键词 移动机器人 蒙特卡罗定位 粒子滤波 无迹卡尔曼滤波 Mobile robot Monte Carlo localization（MCL） particle filter unscented Kalman filter
分类号 TP242 [自动化与计算机技术—检测技术与自动化装置]
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参考文献17

  • 1Sanjeev A, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/nomGaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
  • 2Dellaertt F, Fox D, Burgaxd W, Thrun S. Monte Carlo localization for mobile robots. In: Proceedings of IEEE Inter- national Conference on Robotics and Automation. Detroit, USA: IEEE, 1999. 1322-1328.
  • 3Thrun S, Fox D, Burgard W, Dellmert F. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 2001, 128(1-2): 99-141.
  • 4Fox D. Adapting the sample size in particle filters through KLD-sampling. International Journal of Robotics Research, 2003, 22(12): 985-1103.
  • 5Lenser S, Veloso M. Sensor resetting localization for poorly modeled mobile robots. In: Proceedings of IEEE International Conference on Robotics and Automation. San Francisco, USA: IEEE, 2000. 1225-1232.
  • 6Ueda R, Arai T, Sakamoto K, Kikuchi T, Kamiya S. Expansion resetting for recovery from fatal error in Monte Carlo localization-comparison with sensor resetting methods. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendai, Japan: IEEE, 2004. 2481-2486.
  • 7Khan Z, Balch T, Dellaert F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1805-1918.
  • 8van der Merwe R, de Freitas N, Doucet A, Wan E. The Unscented Particle Filter, Technical Report CUED/FINFENG/TR 380, Department of Engineering, Cambridge University, UK, 2000.
  • 9Wu H, Sun F C, Liu H P. Fuzzy particle filtering for uncertain systems. IEEE Transactions on Fuzzy Systems, 2008, 16(5): 1114-1129.
  • 10Pfag P, Plagemann C, Burgard W. Improved likelihood models for probabilistic localization based on range scans. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, USA: IEEE, 2007. 2192-2197.

二级参考文献31

  • 1Thrun S. Probabilistic algorithms in robotics. AI Magazine, 2000, 21(4): 93-109
  • 2Rudy N. Robot Localization and Kalman Filters. [Masterd issertation], Utrecht University: Institute of Information and Computing Sciences, 2003
  • 3Dellaert F, Burgard W, Fox D, Thrun S. Using the condensation algorithm for robust vision-based mobile robot localization. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE,1999. 2:588-594
  • 4Ioannis It. A particle filter tutorial for mobile robot localization. Technical Report (No. TR-CIM-04-02), Centre for Intelligent Machines. McGill University, Canada, 2004
  • 5Fox D. Adapting the sample size in particle filters through KLD-sampling. The International Journal of Robotics Research, 2003, 22(12): 985-1003
  • 6Lenser S, Veloso M. Sensor resetting localization for poorly modelled mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE,2000. 2:1225-1232
  • 7Thrun S, Burgard W, Fox D. Probabilistic Robotics. London: MIT Press, 2005, 189-279
  • 8Kwolek B. Finding location using a particle filter and histogram matching. Artificial Intelligence and Soft Computing. Berlin: Springer, 2004. 786-791
  • 9Lee D, Chung W, Kim M. Probabilistic localization of the service robot by map matching algorithm. In: Proceedings of 2002 International Conference on Control, Automationand Systems, Muju, Korea, Oct 2002. 1607-1627
  • 10Carpenter J, Clifford P, Fearnhead P. An improved particle filter for non-linear problems. Technical Report, Department of Statistics, University of Oxford, 1997

共引文献25

同被引文献337

引证文献28

二级引证文献341

自动化学报
2010年 第6期

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