An Improvement in the Observation Model for Monte Carlo Localization

Anas W. Alhashimi, Roland Hostettler, Thomas Gustafsson

2014

Abstract

Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.

References

  1. Burgard, W. and Thrun, S. (1999). Markov localization for mobile robots in dynamic environments dieter fox dfox@ cs. cmu. edu computer science department and robotics institute carnegie mellon university. Journal of Artificial Intelligence Research, 11:391-427.
  2. Dellaert, F., Fox, D., Burgard, W., and Thrun, S. (1999). Monte carlo localization for mobile robots. In Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on, volume 2, pages 1322-1328. IEEE.
  3. Fox, D., Burgard, W., Dellaert, F., and Thrun, S. (1999). Monte carlo localization: Efficient position estimation for mobile robots. AAAI/IAAI, 1999:343-349.
  4. Olufs, S. and Vincze, M. (2009). An efficient area-based observation model for monte-carlo robot localization. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pages 13-20. IEEE.
  5. Pfaff, P., Burgard, W., and Fox, D. (2006). Robust montecarlo localization using adaptive likelihood models. In European robotics symposium 2006, pages 181-194. Springer.
  6. Pfaff, P., Plagemann, C., and Burgard, W. (2007). Improved likelihood models for probabilistic localization based on range scans. In Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, pages 2192-2197. IEEE.
  7. Pfaff, P., Plagemann, C., and Burgard, W. (2008a). Gaussian mixture models for probabilistic localization. In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages 467-472. IEEE.
  8. Pfaff, P., Stachniss, C., Plagemann, C., and Burgard, W. (2008b). Efficiently learning high-dimensional observation models for monte-carlo localization using gaussian mixtures. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 3539-3544. IEEE.
  9. Plagemann, C., Kersting, K., Pfaff, P., and Burgard, W. (2007). Gaussian beam processes: A nonparametric bayesian measurement model for range finders. In Robotics: Science and Systems.
  10. Rowekamper, J., Sprunk, C., Tipaldi, G. D., Stachniss, C., Pfaff, P., and Burgard, W. (2012). On the position accuracy of mobile robot localization based on particle filters combined with scan matching. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 3158-3164. IEEE.
  11. Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic robotics. MIT Press.
  12. Thrun, S., Fox, D., Burgard, W., and Dellaert, F. (2001). Robust monte carlo localization for mobile robots. Artificial Intelligence, 128(1):99-141.
  13. Vaughan, R. (2008). Massively multi-robot simulation in stage.
  14. Willow Garage, S. A. I. L. (2012). The robot operating system.
  15. Yilmaz, S., Kayir, H. E., Kaleci, B., and Parlaktuna, O. (2010). A new sensor model for particle-filter based localization in the partially unknown environments. In Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, pages 428-434. IEEE.
Download


Paper Citation


in Harvard Style

W. Alhashimi A., Hostettler R. and Gustafsson T. (2014). An Improvement in the Observation Model for Monte Carlo Localization . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 498-505. DOI: 10.5220/0005065604980505


in Bibtex Style

@conference{icinco14,
author={Anas W. Alhashimi and Roland Hostettler and Thomas Gustafsson},
title={An Improvement in the Observation Model for Monte Carlo Localization},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={498-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005065604980505},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - An Improvement in the Observation Model for Monte Carlo Localization
SN - 978-989-758-040-6
AU - W. Alhashimi A.
AU - Hostettler R.
AU - Gustafsson T.
PY - 2014
SP - 498
EP - 505
DO - 10.5220/0005065604980505