Abstract
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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
R. Siegwart and I. Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT 2004.
S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT, London, 2005.
L. Kleeman, “Ultrasonic autonomous robot localization system,” Proc. of IEEE International Conference Intelligent Robots and Systems, Tsukuba, JAPAN, pp. 212–219, 1989.
M. S. Grewal and A. P. Andrews, Kalman Filter: Theory and Practice Using MATLAB, Wieley-Interscience, Canada, 2001.
B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House Publisher, Boston, 2004.
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.
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.
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.
A. Doucet, N. de Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice, Springer-Verlag, 2001.
D. Fox, “Adapting the sample size in particle filters through KLD-sampling,” IEEE Trans. Robotics, vol. 22, no. 12, pp. 985–1003, 2003.
D. Fox, W. Burgard, F. Dellaert, and S. Thrum, “Monte carlo localization: efficient position estimation for mobile robots,” Proc. Conf. Artifical Intelligence, 1999.
R. Siegwart and I. Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT, 2004.
N. Johnson, S. Kotz, and N. Balakrishnan, Continuous Univariate Distributions, John Wiley & Sons, New York, 1994.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Editorial Board member Jang Myung Lee under the direction of Editor Jae-Bok Song. The authors would like to acknowledge the Ministry of Land, Transport and Maritime Affairs, Korea. This work is supported by the 2006 High-Tech Fusion Construction Technology Development Program [06 High-Tech Fusion D01].
Sang-Hyuk Park received his B.S. degree in Electrical Engineering from Hongik University in 2006 and his M.S degree in Electrical Engineering from Korea University in 2008. His research interests include optimal control, robust control, and particle filtering of autonomous mobile robots.
Young-Joong Kim received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Korea University, Seoul, in 1999, 2001, and 2006, respectively. Since 2006, he has been a Postdoctoral Fellow in the School of Electrical Engineering at Korea University. His research interests include optimal control, robust control, and visual control of autonomous mobile robots. He is a member of KIEE.
Myo-Taeg Lim received his B.S. and M.S. degrees in Electrical Engineering from Korea University in Seoul, in 1985 and 1987, respectively. He also received his M.S. and Ph.D. degrees in Electrical Engineering from Rutgers University, U.S.A., in 1990 and 1994, respectively. Since 1996, he has been a Professor in the School of Electrical Engineering at Korea University. His research interests include optimal and robust control, vision based motion control, and autonomous mobile robots. He is the author or coauthor of more than 30 journal papers and two books — Optimal Control of Singularly Perturbed Linear Systems and Application: High-Accuracy Techniques, Control Engineering Series, Marcel Dekker, New York, 2001; Optimal Control: Weakly Coupled Systems and Applications, Automation and Control Engineering Series, CRC Press, New York, 2009. He is a member of ICROS, KIEE and IEEE.
Rights and permissions
About this article
Cite this article
Park, SH., Kim, YJ. & Lim, MT. Novel adaptive particle filter using adjusted variance and its application. Int. J. Control Autom. Syst. 8, 801–807 (2010). https://doi.org/10.1007/s12555-010-0412-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-010-0412-4