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Simultaneous Robot Localization and Mapping Based on a Visual Attention System

  • Simone Frintrop
  • Patric Jensfelt
  • Henrik Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

Visual attention regions are useful for many applications in the field of computer vision and robotics. Here, we introduce an application to simultaneous robot localization and mapping. A biologically motivated attention system finds regions of interest which serve as visual landmarks for the robot. The regions are tracked and matched over consecutive frames to build stable landmarks and to estimate the 3D position of the landmarks in the environment. Matching of current landmarks to database entries enables loop closing and global localization. Additionally, the system is equipped with an active camera control, which supports the system with a tracking, a re-detection, and an exploration behaviour. We present experiments which show the applicability of the system in a real-world scenario. A comparison between the system operating in active and in passive mode shows the advantage of active camera control: we achieve a better distribution of landmarks as well as a faster and more reliable loop closing.

Keywords

Salient Region Passive Mode Simultaneous Localization Loop Closing Visual Slam 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aloimonos, Y., Weiss, I., Bandopadhay, A.: Active vision. International Journal of Computer Vision (IJCV) 1(4), 333–356 (1988)CrossRefGoogle Scholar
  2. 2.
    Backer, G., Mertsching, B., Bollmann, M.: Data- and model-driven gaze control for an active-vision system. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 23(12), 1415–1429 (2001)CrossRefGoogle Scholar
  3. 3.
    Clemente, L.A., Davison, A.J., Reid, I.D., neira, J., Tardos, J.D.: Mapping large loops with a single hand-held camera. In: Proc. of Robotics: Science and Systems (RSS) (2007)Google Scholar
  4. 4.
    Cummins, M., Newman, P.: Probabilistic appearance based navigation and loop closing. In: ICRA 2007. Proc. IEEE International Conference on Robotics and Automation, Rome, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  5. 5.
    Davison, A., Murray, D.: Mobile robot localisation using active vision. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 809–828. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Davison, A., Murray, D.: Simultaneous localisation and map-building using active vision. IEEE Trans. PAMI  (2002)Google Scholar
  7. 7.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. of the ICCV (October 2003)Google Scholar
  8. 8.
    Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Trans. Robot. Automat. 17(3), 229–241 (2001)CrossRefGoogle Scholar
  9. 9.
    Frese, U., Larsson, P., Duckett, T.: A multigrid algorithm for simultaneous localization and mapping. IEEE Trans. Robot. 21(2), 1–12 (2005)CrossRefGoogle Scholar
  10. 10.
    Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. LNCS (LNAI), vol. 3899, Springer, Heidelberg (2006) (PhD thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany, July 2005)Google Scholar
  11. 11.
    Frintrop, S., Backer, G., Rome, E.: Goal-directed search with a top-down modulated computational attention system. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) Pattern Recognition. LNCS, vol. 3663, pp. 117–124. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Frintrop, S., Cremers, A.B.: Top-down attention supports visual loop closing. In: ECMR 2005. Accepted for Proc. of European Conference on Mobile Robotics (2007)Google Scholar
  13. 13.
    Frintrop, S., Jensfelt, P., Christensen, H.: Attentional Landmark Selection for Visual SLAM. In: IROS 2006. Proc. of the International Conference on Intelligent Robots and Systems, Beijing, China, (October 2006)Google Scholar
  14. 14.
    Frintrop, S., Klodt, M., Rome, E.: A real-time visual attention system using integral images. In: ICVS. Proc. of the 5th International Conference on Computer Vision Systems, Bielefeld, Germany (March 2007)Google Scholar
  15. 15.
    Goncavles, L., di Bernardo, E., Benson, D., Svedman, M., Ostrovski, J., Karlsson, N., Pirjanian, P.: A visual front-end for simultaneous localization and mapping. In: Proc. of ICRA, pp. 44–49 (April 2005)Google Scholar
  16. 16.
    Ho, K., Newman, P.: Detecting loop closure with scene sequences. International Journal of Computer Vision and International Journal of Robotics Research. Joint issue on computer vision and robotics  (2007)Google Scholar
  17. 17.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  18. 18.
    Jensfelt, P., Kragic, D., Folkesson, J., Björkman, M.: A framework for vision based bearing only 3D SLAM. In: Proc. of ICRA 2006, Orlando, FL (May 2006)Google Scholar
  19. 19.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. of ICCV, pp. 1150–1157 (1999)Google Scholar
  20. 20.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proc. of ICCV, pp. 525–531 (2001)Google Scholar
  21. 21.
    Navalpakkam, V., Rebesco, J., Itti, L.: Modeling the influence of task on attention. Vision Research 45(2), 205–231 (2005)CrossRefGoogle Scholar
  22. 22.
    Newman, P., Ho, K.: SLAM- loop closing with visually salient features. In: ICRA 2005. Proc. of the International Conference on Robotics and Automation, Barcelona, Spain (April 2005)Google Scholar
  23. 23.
    Nickerson, S.B., Jasiobedzki, P., Wilkes, D., Jenkin, M., Milios, E., Tsotsos, J.K., Jepson, A., Bains, O.N.: The ARK project: Autonomous mobile robots for known industrial environments. Robotics and Autonomous Systems 25(1-2), 83–104 (1998)CrossRefGoogle Scholar
  24. 24.
    Ouerhani, N., Bur, A., Hügli, H.: Visual attention-based robot self-localization. In: ECMR 2005. Proc. of European Conference on Mobile Robotics, Ancona, Italy, pp. 8–13 (September 2005)Google Scholar
  25. 25.
    Siagian, C., Itti, L.: Biologically-inspired robotics vision monte-carlo localization in the outdoor environment. In: IROS 2007. Proc. of the International Conference on Intelligent Robots and Systems (to appear, 2007)Google Scholar
  26. 26.
    Sun, Y., Fisher, R.: Object-based visual attention for computer vision. Artificial Intelligence 146(1), 77–123 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Thrun, S.: Finding landmarks for mobile robot navigation. In: Proc. of ICRA (1998)Google Scholar
  28. 28.
    Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: Probabilistic algorithms and the interactive museum tour-guide robot minerva. Int’l J. of Robotics Research 19(11) (2000)Google Scholar
  29. 29.
    Thrun, S., Liu, Y., Koller, D., Ng, A.Y., Ghahramani, Y., Durrant-Whyte, H.: Simultaneous localization and mapping with sparse extended information filters. Int. J. Robot. Res. 23(7-8), 693–716 (2004)CrossRefGoogle Scholar
  30. 30.
    Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12, 97–136 (1980)CrossRefGoogle Scholar
  31. 31.
    Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 78(1-2), 507–545 (1995)CrossRefGoogle Scholar
  32. 32.
    Vidal-Calleja, T., Davison, A.J., Andrade-Cetto, J., Murray, D.W.: Active control for single camera slam. In: Proc. of ICRA 2006 (2006)Google Scholar
  33. 33.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision (IJCV) 57(2), 137–154 (2004)CrossRefGoogle Scholar
  34. 34.
    Wolfe, J.M.: Guided search 2.0: A revised model of visual search. Psychonomic Bulletin and Review 1(2), 202–238 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Simone Frintrop
    • 1
  • Patric Jensfelt
    • 2
  • Henrik Christensen
    • 3
  1. 1.Comp. Science III, University of BonnGermany
  2. 2.CSC, KTH, StockholmSweden
  3. 3.GeorgiaTec, AtlantaUSA

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