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Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots

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An Erratum to this article was published on 03 May 2014

Abstract

Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process.

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References

  1. Durrant-Whyte, H.F., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Int. J. Comput. Vis. Image Understand. 110(3), 346–359 (2008)

    Article  Google Scholar 

  3. Harris, C., Stephens, M.: A combined corner and edge detector, In: Proceeding of the 4th Alvey Vision Conference, pp. 147–151 (1988)

  4. Lee, S., Kim, E., Park, Y.: 3D object recognition using multiple features for robotic manipulation. In: Proceeding of the International Conference on Robotics and Automation, pp. 3768–3774. Orlando (2006)

  5. Lowe, D.G.: Distinctive image features from scale-invariant key-points. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  6. Se, S., Lowe, D.G., Little, J.J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Robot. 21(3), 364–375 (2005)

    Article  Google Scholar 

  7. Murillo, A.C., Guerrero, J.J., Sagues, C.: SURF features for efficient robot localization with omnidirectional images. In: Proceedings of the International Conference on Robotics and Automation, pp. 3901–3907. Roma (2007)

  8. Sorenson, H.W., Stubberud, A.R.: Non-linear filtering by approximation of the a posteriori density. Int. J. Control. 8(1), 33–51 (1968)

    Article  MATH  Google Scholar 

  9. Thrun, S.: Bayesian landmark learning for mobile robot localization. Mach. Learn. 33(1), 41–76 (1998)

    Article  MATH  Google Scholar 

  10. Grisetti, G., Stachniss, C., Grzonka, S., Burgard, W.: A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. In: Proceedings of Robotics: Science and Systems, Atalanta (2007)

  11. Konolige, K.: SLAM via variable reduction from constraint maps. In: Proceedings of the International Conference on Robotics and Automation, Barcelona, pp. 667–672 (2005)

  12. Borges, P., Zlot, R., Bosse, M., Nuske, S., Tews, A.: Vision-based localization using an edge map extracted from 3D laser range data. In: Proceedings of the International Conference on Robotics and Automation, pp. 4902–4909. Anchorage (2010)

  13. Zhang, Z.: Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. Vis. 27(2), 161–195 (1998)

    Article  Google Scholar 

  14. Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robot. 4(4), 333–349 (1997)

    Article  Google Scholar 

  15. Bailey, T., Durrant-Whyte, H.F.: Simultaneous localization and mapping: part II. IEEE Robot. Autom. Mag. 13(2), 108–117 (2006)

    Article  Google Scholar 

  16. Martinelli, A., Nguyen, V., Tomatis, N., Siegwart, R.: A relative map approach to SLAM based on shift and rotation invariants. Robot. Auton. Syst. 55(1), 50–61 (2007)

    Article  Google Scholar 

  17. Steder, B., Grisetti, G., Stachniss, C., Burgard, W.: Visual slam for flying vehicles. IEEE Trans. Robot. 24(5), 1088–1093 (2008)

    Article  Google Scholar 

  18. Gutmann, J.S., Konolige, K.: Incremental mapping of large cyclic environments. In: Proceedings of the International Symposium of Computational Intelligence in Robotics and Automation, pp. 318–325. Monterey (1999)

  19. Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge (2000)

  20. Moreno, L., Garrido, S., Munoz, M.L.: Evolutionary filter for robust mobile robot localization. Robot. Auton. Syst. 54(7), 590–600 (2006)

    Article  Google Scholar 

  21. Wolf, J., Burgard, W., Burkhardt, H.: Robust vision-based localization by combining an image retrieval system with monte carlo localization. IEEE Trans. Robot. 21(2), 208–216 (2005)

    Article  Google Scholar 

  22. Nuske, S., Roberts, J., Wyeth, G.: Robust outdoor visual localization using a three-dimensional-edge map. J. Field Robot. 26(9), 728–756 (2009)

    Article  Google Scholar 

  23. Gasparri, A., Panzieri, S., Pascucci, F., Ulivi, G.: Monte carlo filter in mobile robotics localization: a clustered evolutionary point of view. J. Intell. Robotic. Syst. 47(2), 155–174 (2006)

    Article  Google Scholar 

  24. Penne, R., Mertens, L., Veraart, J.: Mobile camera localization using apollonius circles and virtual landmarks. J. Intell. Robotic. Syst. 58(3–4), 287–308 (2010)

    MATH  Google Scholar 

  25. Kalman, R.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  Google Scholar 

  26. Baltzakis, H., Trahanias, P.: Hybrid mobile robot localization using switching state-space models. In: Proceedings of the International Conference on Robotics and Automation, pp. 366–373. Washington D.C. (2002)

  27. Crowley, J.L.: Mathematical foundations of navigation and perception for an autonomous mobile robot. In: Proceedings of the IJCAI Workshop on Reasoning with Uncertainty in Robotics, pp. 9–51. Berlin (1995)

  28. Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (1999)

    Book  MATH  Google Scholar 

  29. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)

  30. Thrun, S., Bennewitz, M., Burgard, W., Cremers, A., Dellaert, F., Fox, D., H¨ahnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: MINERVA: a second generation mobile tour-guide robot. In: Proceedings of the IEEE International Conference on Robotics and Automation, (1999)

  31. Folkesson, J., Jensfelt, P., Christensen, H.: Vision SLAM in the measurement subspace. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 30–35. Barcelona (2005)

  32. Jeong, W.Y., Lee, K.M.: CV-SLAM: A new ceiling vision-based SLAM technique, In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3195–3200. Edmonton (2005)

  33. Matsumoto, T., Takahashi, T., Iwahashi, M., Kimura, T., Salbiah, S., Mokhtar, N.: Visual compensation in localization of a robot on a ceiling map. Sci. Res. Essay 6(1), 131–135 (2011)

    Google Scholar 

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Authors and Affiliations

  1. School of Information and Communication Engineering and Department of Interaction Science, Sungkyunkwan University, Suwon, South Korea

    Seongsoo Lee & Sukhan Lee

  2. Future IT Laboratory, LG Electronics Inc., Seoul, South Korea

    Seongsoo Lee & Seungmin Baek

Authors
  1. Seongsoo Lee
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  2. Sukhan Lee
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  3. Seungmin Baek
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Correspondence to Sukhan Lee.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s10846-014-0047-4.

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Lee, S., Lee, S. & Baek, S. Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots. J Intell Robot Syst 67, 7–24 (2012). https://doi.org/10.1007/s10846-011-9647-4

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  • DOI: https://doi.org/10.1007/s10846-011-9647-4

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