MOBILE ROBOT LOCALIZATION USING LINEAR SYSTEM MODEL

Xu Zezhong, Liu Jilin

2004

Abstract

Localization is a fundamental problem for mobile robot autonomous navigation. EKF is an efficient tool for position estimation, but it suffers from linearization errors due to linear approximation of nonlinear system equations. In this paper we describe a position estimation method for mobile robot. Process and measurement equations are linear by appropriately constructing the state vector and system models. The position of mobile robot is estimated recursively based on optimal KF. It avoids linear approximation of nonlinear system equations and is free of linearization error. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser rangefinder SICK.

References

  1. Alspach, D. and Sorenson, H. 1972. Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Transactions on Automatic Control, vol. AC-17, pp. 439-448.
  2. Bellaire, R.L., Kamen E.W. and Zabin, S.M. 1995. A new nonlinear iterated filter with applications to target tracking. In SPIE Proceedings on Signal and Data Processing of Small Targets, vol. 2561, pp. 240-251.
  3. Borenstein, J. and Feng, L. 1995. Correction of Systematic Odometer Errors in Mobile Robots. In Proceedings of the 1995 International Conference on Intelligent Robots and Systems (lROS'95), Pittsburgh, Pennsylvania, pp. 569-574.
  4. Chui, C.K., Chen, G. and Chui, H. 1990. Modified extended Kalman filtering and parallel system parameter identification, IEEE Transactions on Automatic Control, 35 (1): 100-104.
  5. Fox, D., Burgard, W., Thrun, S. and Cremers, A.B. 1998. Position Estimation for Mobile Robots in Dynamic Environments. AAAI/IAAI 1998: 983-988.
  6. Fox, D., Burgard, W., Dellaert F. and Thrun, S. 1999. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. AAAI/IAAI 1999: 343- 349.
  7. Glielmo, L., Setola R. and Vasca, F. 1999. An Interlaced Extended Kalman Filter, IEEE Transactions on Automatic Control, vol. 44, no. 8, pp. 1546-1549.
  8. Ito, K. and Xiong, K. 2000. Gaussian Filters for Nonlinear Filtering Problems. IEEE Transactions on Automatic Control, 45(5): 910-927.
  9. Julier, S., Uhlmann J. and Durrant-Whyte, H. 1995. A new approach for filtering nonlinear systems. In Proceedings of the American Control Conference, pp.1628-1632.
  10. Kalman, R.E. 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering Transactions of the ASME, pp.35-45.
  11. Kalman, R.E. and Bucy, R. 1961. New results in linear filtering and prediction theory. Journal of Basic Engineering Transactions of the ASME, vol. 83, pp. 95-108.
  12. Kelly, A.J. 1994. A 3D State Space Formulation of a Navigation Kalman Filter for Autonomous Vehicles, CMU Robotics Institute Technical Report CMU-RITR-94-19.
  13. Lefebvre, T., Bruyninckx, H. and De Schutter, J. 2002. Comment on “A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators”. IEEE Transactions On Automatic Control, Vol. 47, no. 8, pp.1406-1408.
  14. Lefebvre, T., Bruyninckx, H. and De Schutter, J. 2003. Non-linear Autonomous Compliant Motion with a Non-minimal State Kalman Filter. In International Conference on advanced Robotics, pp.136-141.
  15. Mehra, R.K. 1971. A Comparison of Several Nonlinear Filters for Reentry Vehicle Tracking. IEEE Transactions on Automatic Control, AC-16(4): 307- 319.
  16. Nam, K.H. and Tahk, M.J. 1999. A second-order stochastic filter involving coordinate transformation. IEEE Transactions on Automatic Control, Vol. 44, No.3, pp. 603-608.
  17. Olson, C.F. 2000. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, vol. 16(1), pp. 55-66.
  18. Thrun, S. 2000. Probabilistic algorithms in robotics, AI Magazine, 21(4): 93-109.
  19. Wall, D.S. and Gaston, F.M.F. 1997. Modified extended kalman filtering. In Proceedings of International Conference on Digital Signal Processing, pp.703-706.
  20. Wan, E.A. and van der Merwe, R. 2000. The unscented Kalman filter for nonlinear estimation. In Proceedings of Conf. Adaptive Systems for Signal Processing, Communication and Control, Canada.
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Paper Citation


in Harvard Style

Zezhong X. and Jilin L. (2004). MOBILE ROBOT LOCALIZATION USING LINEAR SYSTEM MODEL . In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 972-8865-12-0, pages 243-248. DOI: 10.5220/0001136202430248


in Bibtex Style

@conference{icinco04,
author={Xu Zezhong and Liu Jilin},
title={MOBILE ROBOT LOCALIZATION USING LINEAR SYSTEM MODEL},
booktitle={Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2004},
pages={243-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001136202430248},
isbn={972-8865-12-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - MOBILE ROBOT LOCALIZATION USING LINEAR SYSTEM MODEL
SN - 972-8865-12-0
AU - Zezhong X.
AU - Jilin L.
PY - 2004
SP - 243
EP - 248
DO - 10.5220/0001136202430248