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Particle / Kalman Filter for Efficient Robot Localization

by Imbaby I. Mahmoud, May Salama, Asmaa Abd El Tawab
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 2
Year of Publication: 2014
Authors: Imbaby I. Mahmoud, May Salama, Asmaa Abd El Tawab
10.5120/18492-9554

Imbaby I. Mahmoud, May Salama, Asmaa Abd El Tawab . Particle / Kalman Filter for Efficient Robot Localization. International Journal of Computer Applications. 106, 2 ( November 2014), 20-27. DOI=10.5120/18492-9554

@article{ 10.5120/18492-9554,
author = { Imbaby I. Mahmoud, May Salama, Asmaa Abd El Tawab },
title = { Particle / Kalman Filter for Efficient Robot Localization },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 2 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number2/18492-9554/ },
doi = { 10.5120/18492-9554 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:19.403119+05:30
%A Imbaby I. Mahmoud
%A May Salama
%A Asmaa Abd El Tawab
%T Particle / Kalman Filter for Efficient Robot Localization
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 2
%P 20-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a comparison of different fitters namely: Extended Kalman Filter (EKF), Particle Filter (PF) and a proposed Enhanced Particle / Kalman Filter (EPKF) used in robot localization. These filters are implemented in matlab environment and their performances are evaluated in terms of computational time and error from ground truth and the results are reported. The considered robot localizer uses radio beacons that provide the ability to measure range only. Since EKF and its variants are not capable to efficiently solve the global localization problem, we propose the Enhanced Particle / Kalman Filter (EPKF) which provide the required initial location to address this drawback of EKF. We propose using PF as Initialization phase to coarsely predict the initial location and numerous sets of data are experimented to get robust conclusion. The results showed that the proposed localization approach which adopts the particle filter as initialization step to EKF achieves higher accuracy localization while, the computational cost is kept almost as EKF alone.

References
  1. Fox, D. (1999). Markov localization for mobile robots in dynamic environments, Journal of Artificial Intelligence Research: 391-427.
  2. Maskell,S. , Gordon,N. and Clapp T February 2002, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, 50( 2):174-188,
  3. Ioannis ,R. 2004. A particle filter tutorial for mobile robot localization, Technical Report TR-CIM-04-02,McGill University, Canada.
  4. http://oursland. net/projects/fastslam/
  5. Carpenter,J. and Clifford,P. and Fearnhead,P. 1999 An Improved Particle Filter for Non-linear Problems Department of Statistics, University of Oxford.
  6. Maskell, S. , Gordon,N. and Clapp,T. 2002, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, M. Sanjeev Arulampalam, IEEE Transactions on Signal Processing, Vol. 50, NO. 2.
  7. Thrun,S. , Fox,D. , Burgard, W. and Dellaert, F. 2000 Robust Monte Carlo localization for mobile robots, Artificial Intelligence, pp. 99-141.
  8. Liu, J. and Chen, R. 1998 Sequential Monte Carlo methods for dynamic systems, Journal of American Statistical Association, 93.
  9. Doucet,A. , de Freitas,N. , and Gordon,N. ,2001. Sequential Monte Carlo Methods in Practice, Springer.
  10. Sasiadek, J. Z. and Hartana, P. 2000 Sensor data fusion using Kalman filter, InProceedings of the Third International Conference on Information Fusion, vol. 2,pp. 19-25.
  11. Djugash,J. and Singh. S 2009 A Robust Method of Localization and Mapping Using Only Range The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  12. Y. Bar-Shalom, X. Rong Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, 2001.
  13. Abd El-Halym, H. A, Mahmoud, I. I. , AbdelTawab. A, and Habib, S. E. D. 2009 Appraisal of an Enhanced Particle Filter for Object Tracking, Proceedings of IEEE International conference on Image processing (ICIP) Conf. , Cairo, Egypt, pp. 4105-4107.
  14. Ristics, B. , Arulanpulam, S. and Gordon, N. Beyond the Kalman Filter, Artech House, 2004.
  15. Abd El-Halym, H. A. E. -L. , Mahmoud, I. I. , and Habib, S. E. D. 2012. Proposed hardware architectures of particle filter for object tracking. EURASIP Journal on Advances in Signal Processing, 1(17), 1-19.
  16. Abd El-Halym, H. A, Mahmoud, I. I. , AbdelTawab. A, and Habib, S. E. D. 2009 Particle Filter versus Particle Swarm Optimization for Object Tracking,proceedings of ASAT Conf,Cairo Egypt
  17. Isard. M. and Blake,A. 1998 Condensation –, conditional density propagation for visual tracking, International Journal of Computer Vision 29(1), pp. 5-28.
  18. Kurth,D. 2004 Range-Only Robot Localization and SLAM with Radio, Robotics Institute, Carnegie Mellon Univ. , Technical Report CMU-RI-TR-04-29
  19. Mahmoud,T. , Tawab, A. A and Salama, M. 2013 An Enhanced Particle / Kalman Filter for Robot Localization Localization National Radio Science Conference (NRSC) pp 338 - 346 .
Index Terms

Computer Science
Information Sciences

Keywords

Particle Filter Extended Kalman Filter Robot Localization

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