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Journal of Control Theory and Applications

, Volume 9, Issue 4, pp 472–478 | Cite as

Improved Rao-Blackwellized particle filter for simultaneous robot localization and person-tracking with single mobile sensor

  • Kun QianEmail author
  • Xudong Ma
  • Xianzhong Dai
  • Fang Fang
Article

Abstract

A probabilistic algorithm is proposed for the problem of simultaneous robot localization and people-tracking (SLAP) using single onboard sensor in situations with sensor noise and global uncertainties over the observer’s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person’s position is estimated using unscented Kalman filter (UKF) to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.

Keywords

Mobile robot localization People tracking Rao-Blackwellized particle filter Unscented Kalman filter Service robot 

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References

  1. [1]
    S. Thrun, W. Burgard, D. Fox. Probabilistic Robotics. Boston: MIT Press, 2005.zbMATHGoogle Scholar
  2. [2]
    D. Hahnel, D. Schulz, W. Burgard. Mobile robot mapping in populated environments. Advanced Robotics, 2003, 17(7): 579–598.CrossRefGoogle Scholar
  3. [3]
    W. Hu, M. Hu, X. Zhou, et al. Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 663–671.MathSciNetCrossRefGoogle Scholar
  4. [4]
    D. Schulz, W. Burgard. People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research, 2003, 22(2): 99–116.CrossRefGoogle Scholar
  5. [5]
    M. Rosencrantz, G. Gordon, S. Thrun. Locating moving entities in indoor environments with teams of mobile robots. Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems, New York: IEEE, 2003: 233–240.CrossRefGoogle Scholar
  6. [6]
    M. Montemerlo, S. Thrun, W. Whittaker. Conditional particle filters for simultaneous mobile robot localization and people-tracking. Proceedings of IEEE International Conference on Robotics and Automation, New York: IEEE, 2002: 695–701.Google Scholar
  7. [7]
    S. Thrun, D. Fox, W. Burgard. Monte Carlo localization with mixture proposal distribution. Proceedings of AAAI National Conference Artificial Intelligence, New York: IEEE, 2000: 859–865.Google Scholar
  8. [8]
    S. Thrun, D. Fox, W. Burgard, et al. Robust Monte Carlo localization for mobile robots. Artifial Intelligence, 2001, 128(1/2): 99–141.zbMATHCrossRefGoogle Scholar
  9. [9]
    C. C. Wang, C. Thorpe, S. Thrun, et al. Simultaneous localization, mapping and moving object tracking. The International Journal of Robotics Research, 2007, 26(9): 889–916.CrossRefGoogle Scholar
  10. [10]
    A. Doucet, N. de Freitas, K. Murphy, et al. Rao-Blackwellized particle filtering for dynamic Bayesian networks. Proceedings of Conference on Uncertainty in Artificial Intelligence, New York: IEEE, 2000: 176–183.Google Scholar
  11. [11]
    W. L. Lu, K. Okuma, J. J. Little. Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image and Vision Computing, 2009, 27(1/2): 189–205.CrossRefGoogle Scholar
  12. [12]
    G. Grisetti, C. Stachniss, W. Burgard. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Transactions on Robotics, 2007, 23(1): 34–46.CrossRefGoogle Scholar
  13. [13]
    C. Kwok, D. Fox. Map-based multiple model tracking of a moving object. Proceedings of RoboCup Symposium, New York: IEEE, 2004: 18–33.Google Scholar
  14. [14]
    D. Schulz, D. Fox, J. Hightower. People tracking with anonymous and id-sensors using Rao-Blackwellised particle filters. Proceedings of International Joint Conference on Artificial Intelligence, New York: IEEE, 2003: 921–928.Google Scholar
  15. [15]
    J. S. Liu, R. Chen, T. Logvinenko. A Theoretical Framework for Sequential Importance Sampling and Resampling. New York: Springer-Verlag, 2001.Google Scholar
  16. [16]
    C. Andrieu, D. N. Freitas, A. Doucet, et al. An introduction to MCMC for machine learning. Machine Learning, 2003, 50(1): 5–43.zbMATHCrossRefGoogle Scholar
  17. [17]
    Y. Rui, Y. Chen. Better proposal distributions: object tracking using unscented particle filter. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, New York: IEEE, 2001: 786–794.Google Scholar
  18. [18]
    M. Scheutz, J. McRaven, G. Cserey. Fast, reliable, adaptive, bimodal people tracking for indoor environments. Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), New York: IEEE, 2004: 1347–1352.Google Scholar
  19. [19]
    K. Qian, X. Ma, X. Dai. Simultaneous robot localization and person tracking using Rao-Blackwellised particle filters with multi-modal sensors. Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), New York: IEEE, 2008: 3452–3457.CrossRefGoogle Scholar

Copyright information

© South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing JiangsuChina

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