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A Grid-Based Monte Carlo Localization with Hierarchical Free-Form Scan Matching

  • Mei Wu
  • Hongbin MaEmail author
  • Xinghong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)

Abstract

This paper is concerned with the localization problem of robot in unstable environment. To improve the accuracy of the localization result in the environment, a grid-based Monte Carlo localization algorithm with hierarchical free-form scan matching (MCL-HF) is proposed. In MCL-HF algorithm the distribution of the samples is adapted by the most recent observation. To simplify the process of the adaptation a feature-based scan-matching algorithm is adopted in the grid-based localization algorithm in a hierarchical free-form. The advantage of the proposed algorithm is demonstrated through the experiment results.

Keywords

Localization Particle filter Scan-matching 

Notes

Acknowledgement

This work is partially supported by National Key Research and Development Program of China under Grant 2017YFF0205306, National Nature Science Foundation of China under Grant 91648117, and Beijing Natural Science Foundation under Grant 4172055.

References

  1. 1.
    Smith, R., Cheeseman, P.: On the representation and estimation of spatial uncertainty. J. Robot. Res. 5(4), 231–238 (1986).  https://doi.org/10.1177/027836498600500404CrossRefGoogle Scholar
  2. 2.
    Durrantwhyte, H.F., Bailey, T.: Simultaneous localization and mapping: part I. J. Robot. Autom. Mag. 13(2), 99–110 (2006).  https://doi.org/10.1109/MRA.2006.1638022CrossRefGoogle Scholar
  3. 3.
    Bailey, T., Durrantwhyte, H.F.: Simultaneous localization and mapping (SLAM): part II. J. Robot. Autom. Mag. 13(3), 108–117 (2006).  https://doi.org/10.1109/MRA.2006.1678144CrossRefGoogle Scholar
  4. 4.
    Huang, S., Dissanayake, G.: Convergence and consistency analysis for extended Kalman filter based SLAM. J. Robot. 23, 1036–1049 (2007)Google Scholar
  5. 5.
    Wang, H., Fu, G., Li, J., et al.: An adaptive UKF based SLAM method for unmanned underwater vehicle. J. Control Decis. 2013, 1–12 (2013).  https://doi.org/10.1155/2013/605981CrossRefzbMATHGoogle Scholar
  6. 6.
    Montemerlo, M., Thrun, S., Roller, D., et al.: Fast SLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1151–1156 (2003) Google Scholar
  7. 7.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. J. Proc. IEEE 92(3), 401–422 (2004).  https://doi.org/10.1109/JPROC.2003.823141CrossRefGoogle Scholar
  8. 8.
    Garrido, S., Moreno, L., Blanco, D.: Exploration and mapping using the VFM motion planner. J. Instrum. Meas. 58(8), 2880–2892 (2009).  https://doi.org/10.1109/TIM.2009.2016372CrossRefGoogle Scholar
  9. 9.
    Ma, Y., Ju, H., Cui, P.: Research on localization and mapping for lunar rover based on RBPF-SLAM, pp. 2880–2892 (2009).  https://doi.org/10.1109/IHMSC.2009.200
  10. 10.
    Sheng, X., Hu, Y.H.: Distributed particle filters for wireless sensor network target tracking. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 845–848 (2005).  https://doi.org/10.1109/ICASSP.2005.1416141
  11. 11.
    Sheng, X., Hu, Y.-H., Ramanathan, P.: Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network. J. Inf. Process. Sensor Netw., 181–188 (2005).  https://doi.org/10.1109/IPSN.2005.1440923
  12. 12.
    Zuo, L., Mehrotra, K.G., Varshney, P.K., et al.: Band width-efficient target tracking in distributed sensor networks using particle filters. In: International Conference on Information Fusion, pp. 1–4 (2006).  https://doi.org/10.1007/s12243-010-0224-9CrossRefGoogle Scholar
  13. 13.
    Gu, D., Sun, J., Hu, Z., et al.: Consensus based distributed particle filter in sensor networks. In: International Conference on Information and Automation, pp. 302–307 (2008).  https://doi.org/10.1109/ICINFA.2008.4608015
  14. 14.
    Santos, J., Portugal, D., Rocha, R.P.: An evaluation of 2D SLAM techniques available in robot operating system. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 1–6 (2013).  https://doi.org/10.1109/SSRR.2013.6719348
  15. 15.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. J. IEEE Trans. Robot. 23(1), 34–46 (2007).  https://doi.org/10.1109/TRO.2006.889486CrossRefGoogle Scholar
  16. 16.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  17. 17.
    Fox, D.: KLD-sampling: adaptive particle filters. J. Adv. Neural Inf. Process. Syst., 713–720 (2001)Google Scholar
  18. 18.
    Rowekamper, J., Sprunk, C., Tipaldi, G.D., et al.: On the position accuracy of mobile robot localization based on particle filters combined with scan matching. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3158–3164 (2012).  https://doi.org/10.1109/IROS.2012.6385988

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of AutomationBeijing Institute of TechnologyBeijingChina
  2. 2.Department of Automatic ControlHenan Institute of TechnologyXinxiangChina

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