Journal of Intelligent & Robotic Systems

, Volume 80, Issue 3–4, pp 641–656 | Cite as

Enhanced Monte Carlo Localization with Visual Place Recognition for Robust Robot Localization

Article

Abstract

This paper proposes extending Monte Carlo Localization methods with visual place recognition information in order to build a robust robot localization system. This system is aimed to work in crowded and non-planar scenarios, where 2D laser rangefinders may not always be enough to match the robot position within the map. Thus, visual place recognition will be used in order to obtain robot position clues that can be used to detect when the robot is lost and also to reset its positions to the right one. The paper presents experimental results based on datasets gathered with a real robot in challenging scenarios.

Keywords

Monte Carlo localization Long-term localization Robust localization Crowded environment 

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References

  1. 1.
    Alcantarilla, P.F., Stasse, O., Druon, S., Bergasa, L.M., Dellaert, F.: How to localize humanoids with a single camera? Auton. Robots 34(1–2), 47–71 (2013). doi: 10.1007/s10514-012-9312-1 CrossRefGoogle Scholar
  2. 2.
    Angeli, A., Filliat, D., Doncieux, S., Meyer, J.-A.: Fast and incremental method for loop-closure detection using bags of visual words. IEEE Trans. Robot. 24(5), 1027–1037 (2008). doi: 10.1109/TRO.2008.2004514 CrossRefGoogle Scholar
  3. 3.
    Carlone, L., Bona, B.: A comparative study on robust localization: Fault tolerance and robustness test on probabilistic filters for range-based positioning. In: International Conference on Advanced Robotics, 2009. ICAR 2009, pp. 1–8 (2009)Google Scholar
  4. 4.
    Corke, P., Paul, R., Churchill, W., Newman, P.: Dealing with shadows: Capturing intrinsic scene appearance for image-based outdoor localisation. In: IROS, pp. 2085–2092. IEEE (2013)Google Scholar
  5. 5.
    Dayoub, F., Duckett, T.: An adaptive appearance-based map for long-term topological localization of mobile robots. In: IEEE/RSJ International Conference onIntelligent Robots and Systems, 2008. IROS 2008, pp. 3364–3369 (2008).Google Scholar
  6. 6.
    Doucet, A., Freitas, N.D., Murphy, K.P., Russell, S.J.: Rao-blackwellised particle filtering for dynamic bayesian networks. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, UAI ’00, pp. 176–183. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2000). http://dl.acm.org/citation.cfm?id=647234.720075
  7. 7.
    Galvez-Lopez, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28(5), 1188–1197 (2012). doi: 10.1109/TRO.2012.2197158 CrossRefGoogle Scholar
  8. 8.
    Glover, A.J., Maddern, W.P., Warren, M., Reid, S., Milford, M., Wyeth, G.: Openfabmap: An open source toolbox for appearance-based loop closure detection. In: ICRA, pp. 4730–4735. IEEE (2012)Google Scholar
  9. 9.
    Hentschel, M., Wagner, B.: An adaptive memory model for long-term navigation of autonomous mobile robots. In: Journal of Robotics (2011)Google Scholar
  10. 10.
    Himstedt, M., Keil, S., Hellbach, S., Böhme, H.J.: A robust graph-based framework for building precise maps from laser range scans. In: ICRA, Workshop on Robust and Multimodal Inference in Factor Graphs (2013)Google Scholar
  11. 11.
    Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. In: Proc. International Conference on Robotics and Automation, ICRA, pp. 3607–3613. IEEE (2011) http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5979949
  12. 12.
    Kümmerle, R., Ruhnke, M., Steder, B., Stachniss, C., Burgard, W.: A navigation system for robots operating in crowded urban environments. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3225–3232 (2013)Google Scholar
  13. 13.
    Pérez-Lara, J., Caballero, F., Merino, L.: Long-term ground robot localization architecture for mixed indoor-outdoor scenarios. In: Proceedings of the International Symposium on Robotics, ISR (2014)Google Scholar
  14. 14.
    Pérez-Lara, J., Caballero, F., Merino, L.: Integration of Monte Carlo localization and place recognition for reliable long term robot localization. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2014). Espinho, Portugal (2014)Google Scholar
  15. 15.
    Ruhnke, M., Kümmerle, R., Grisetti, G., Burgard, W.: Highly accurate maximum likelihood laser mapping by jointly optimizing laser points and robot poses. In: Proceedings International Conference on Robotics and Automation, ICRA (2011)Google Scholar
  16. 16.
    Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, C.: Probabilistic algorithms and the interactive museum tour-guide robot minerva. Int. J. Robot. Res. 19, 972–999 (2000)CrossRefGoogle Scholar
  17. 17.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (2005)Google Scholar
  18. 18.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artif. Intell. 128(1–2), 99–141 (2000)Google Scholar
  19. 19.
    Wallach, H.M.: Topic modeling: Beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, ICML ’06, pp. 977–984. ACM, New York, NY, USA (2006). doi: 10.1145/1143844.1143967

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Javier Pérez
    • 1
  • Fernando Caballero
    • 2
  • Luis Merino
    • 1
  1. 1.University Pablo de OlavideSevilleSpain
  2. 2.University of SevilleSevilleSpain

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