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On Environmental Model-Based Visual Perception for Humanoids

  • D. Gonzalez-Aguirre
  • S. Wieland
  • T. Asfour
  • R. Dillmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

In this article an autonomous visual perception framework for humanoids is presented. This model-based framework exploits the available knowledge and the context acquired during global localization in order to overcome the limitations of pure data-driven approaches. The reasoning for perception and the properceptive components are the key elements to solve complex visual assertion queries with a proficient performance. Experimental evaluation with the humanoid robot ARMAR-IIIa is presented.

Keywords

Model-Based Vision Object Recognition Humanoids 

References

  1. 1.
    Okada, K., Kojima, M., Sagawa, Y., Ichino, T., Sato, K., Inaba, M.: Vision based behavior verification system of humanoid robot for daily environment tasks. In: IEEE-RAS Int. Conference on Humanoid Robots (2006)Google Scholar
  2. 2.
    Okada, K., Kojima, M., Tokutsu, S., Maki, T., Mori, Y., Inaba, M.: Multi-cue 3D object recognition in knowledge-based vision-guided humanoid robot system. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 2007 (2007)Google Scholar
  3. 3.
    Okada, K., Tokutsu, S., Ogura, T., Kojima, M., Mori, Y., Maki, T., Inaba, M.: Scenario controller for daily assistive humanoid using visual verification, task planning and situation reasoning. Intelligent Autonomous Systems 10 (2008) ISBN 978-1-58603-887-8Google Scholar
  4. 4.
    Okada, K., Kojima, M., Tokutsu, S., Mori, Y., Maki, T., Inaba, M.: Task guided attention control and visual verification in tea serving by the daily assistive humanoid HRP2JSK. In: IROS 2008. IEEE/RSJ International Conference on Intelligent Robots and Systems (2008)Google Scholar
  5. 5.
    Patnaik, S.: Robot Cognition and Navigation: An Experiment with Mobile Robots. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  6. 6.
    Gonzalez-Aguirre, D., Asfour, T., Bayro-Corrochano, E., Dillmann, R.: Model-based visual self-localization using geometry and graphs. In: 19th International Conference on Pattern Recognition. ICPR 2008 (2008)Google Scholar
  7. 7.
    Gonzalez-Aguirre, D., Asfour, T., Bayro-Corrochano, E., Dillmann, R.: Improving Model-Based Visual Self-Localization using Gaussian Spheres. In: 3rd International Conference on Applications of Geometric Algebras in Computer Science and Engineering. AGACSE 2008 (2008)Google Scholar
  8. 8.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, San Francisco (2004)zbMATHGoogle Scholar
  9. 9.
    The Integrating Vision Toolkit (IVT), http://ivt.sourceforge.net/
  10. 10.
    Asfour, T., Regenstein, K., Azad, P., Schroder, J., Bierbaum, A., Vahrenkamp, N., Dillmann, R.: ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control. In: IEEE-RAS Int. Conference on Humanoid Robots (2006)Google Scholar
  11. 11.
    Gordon, G., Billinghurst, M., Bell, M., Woodfill, J., Kowalik, B., Erendi, A., Tilander, J.: The use of dense stereo range data in augmented reality. In: Proceedings of International Symposium on Mixed and Augmented Reality, 2002. ISMAR 2002, pp. 14–23 (2002)Google Scholar
  12. 12.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  13. 13.
    Prats, M., Wieland, S., Asfour, T., del Pobil, A.P., Dillmann, R.: Compliant interaction in household environments by the Armar-III humanoid robot. In: IEEE-RAS Int. Conference on Humanoid Robots (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • D. Gonzalez-Aguirre
    • 1
  • S. Wieland
    • 1
  • T. Asfour
    • 1
  • R. Dillmann
    • 1
  1. 1.Institute for AnthropomaticsUniversity of KarlsruheKarlsruheGermany

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