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International Journal of Control, Automation and Systems v.8 no.4, 2010년, pp.801 - 807   SCIE
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Novel Adaptive Particle Filter Using Adjusted Variance and Its Application

Park, Sang-Hyuk    (School of Electrical Engineering, Korea University   ); Kim, Young-Joong    (School of Electrical Engineering, Korea University   ); Lim, Myo-Taeg    (School of Electrical Engineering, Korea University  );
  • 초록

    Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.


  • 주제어

    Kullback-leibler distance .   mobile robot .   particle filter .   ultrasonic beacon.  

  • 참고문헌 (14)

    1. R. Siegwart and I. Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT 2004. 
    2. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT, London, 2005. 
    3. L. Kleeman, "Ultrasonic autonomous robot localization system," Proc. of IEEE International Conference Intelligent Robots and Systems, Tsukuba, JAPAN, pp. 212-219, 1989. 
    4. M. S. Grewal and A. P. Andrews, Kalman Filter: Theory and Practice Using MATLAB, Wieley Interscience, Canada, 2001. 
    5. B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House Publisher, Boston, 2004. 
    6. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear// non-gaussian bayesian tracking," IEEE Trans. Signal Processing, vol. 50, no. 2, pp. 174-188, Feb. 2002. 
    7. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," Proc. IEE Rader and Signal Processing, vol. 140, pp. 107-113, Apr. 1993. 
    8. Y. Liu, B. Wang, W. He, J. Zhao, and Z. Ding, "Fundamental principles and applications of particle filters," Proc. of the Conf. Intelligent Control and Automation, pp. 5327-5331, Jun. 2006. 
    9. A. Doucet, N. de Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice, Springer-Verlag, 2001. 
    10. D. Fox, "Adapting the sample size in particle filters through KLD-sampling," IEEE Trans. Robotics, vol. 22, no. 12, pp. 985-1003, 2003. 
    11. D. Fox, W. Burgard, F. Dellaert, and S. Thrum, "Monte carlo localization: efficient position estimation for mobile robots," Proc. Conf. Artifical Intelligence, 1999. 
    12. R. Siegwart and I. Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT, 2004. 
    13. N. Johnson, S. Kotz, and N. Balakrishnan, Continuous Univariate Distributions, John Wiley & Sons, New York, 1994. 
    14. Y. J. Lee, B. D. Yim, and J. B. Song, "Mobile robot localization based on effective combination of vision and range sensors," Int. J. of Control, Automation, and Systems, vol. 7, no. 1, pp. 97-104, 2009.     

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