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An Integration Concept for Vision-Based Object Handling: Shape-Capture, Detection and Tracking

  • Matthias J. Schlemmer
  • Georg Biegelbauer
  • Markus Vincze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)

Abstract

Combining visual shape-capturing and vision-based object manipulation without intermediate manual interaction steps is important for autonomic robotic systems. In this work we introduce the concept of such a vision system closing the chain of shape-capturing, detecting and tracking. Therefore, we combine a laser range sensor for the first two steps and a monocular camera for the tracking step. Convex shaped objects in everyday cluttered and occluded scenes can automatically be re-detected and tracked, which is suitable for automated visual servoing or robotic grasping tasks. The separation of shape and appearance information allows different environmental and illumination conditions for shape-capturing and tracking. The paper describes the framework and its components of visual shape-capturing, fast 3D object detection and robust tracking. Experiments show the feasibility of the concept.

Keywords

Interest Point Range Image Visual Servoing Integration Concept Monocular Camera 
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.
    Barr, A.H.: Superquadrics and Angle Preserving Transformations. IEEE Computer Graphics and Applications 1(1), 11–23 (1981)CrossRefGoogle Scholar
  2. 2.
    Biegelbauer, G., Vincze, M.: Fast and Robust 3D Object Detetction Using a Simplified Superquadric Model Description. In: Proceedings of the 7th Conference on Optical 3-D Measurement Techniques, vol. 2, pp. 220–230 (2005)Google Scholar
  3. 3.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Haverinen, J., Röning, J.: A 3-D Scanner Capturing Range and Color for the Robotics Applications. In: 24th Workshop of the Austrian Association of Pattern Recognition OEAGM/AAPR, pp. 41–48 (2000)Google Scholar
  5. 5.
    Jang, H.-Y., et al.: A Visibility-Based Accessibility Analysis of the Grasp Points for Real-Time Manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3111–3116 (2005)Google Scholar
  6. 6.
    Kim, S., et al.: Robust model-based 3D object recognition by combining feature matching with tracking. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 2123–2128 (2003)Google Scholar
  7. 7.
    Kragic, D., Christensen, H.I.: Model based techniques for robotic servoing and grasping. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System, vol. 1, pp. 299–304 (2002)Google Scholar
  8. 8.
    Leonardis, A., Jaklic, A.: Superquadrics for segmenting and modeling range data. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(11), 1289–1295 (1997)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Lu, C.P., et al.: Fast and Globally Convergent Pose Estimation from Video Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(6), 610–622 (2000)CrossRefGoogle Scholar
  11. 11.
    Mikolajczyk, K., et al.: A Comparison of Affine Region Detectors. International Journal of Computer Vision 65(1/2), 43–72 (2005)CrossRefGoogle Scholar
  12. 12.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  13. 13.
    Moré, J.J.: The Levenberg-Marquardt Algorithm: Implementation and Theory. Numerical Analysis - Lecture Notes in Mathematics, vol. 630, pp. 105–116. Springer, Heidelberg (1977)Google Scholar
  14. 14.
    Mukherjee, S., Nayar, S.K.: Automatic generation of GRBF networks for visual learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 794–800 (1995)Google Scholar
  15. 15.
    Parhami, B.: Voting Algorithms. Machine Learning (IEEE Transactions on Reliability) 43(4), 617–629 (1994)Google Scholar
  16. 16.
    Salganicoff, M.: Active Learning for Vision-Based Robot Grasping. Machine Learning 23(2), 251–278 (1996)Google Scholar
  17. 17.
    Solina, F., Bajcsy, R.: Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(2), 131–147 (1990)CrossRefGoogle Scholar
  18. 18.
    Taylor, G., Kleeman, L.: Integration of robust visual perception and control for a domestic humanoid robot. In: Proceedings IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, vol. 1, pp. 1010–1015 (2004)Google Scholar
  19. 19.
    Yoon, Y., et al.: A New Approach to the Use of Edge Extremities for Model-based Object Tracking. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1883–1889 (2005)Google Scholar
  20. 20.
    Zillich, M., Al-Ani, E.: Camcalb: A user friendly camera calibration software. In: Workshop of the Austrian Association of Pattern Recognition OEAGM/AAPR, pp. 111–116 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Matthias J. Schlemmer
    • 1
  • Georg Biegelbauer
    • 1
  • Markus Vincze
    • 1
  1. 1.Automation and Control InstituteVienna University of TechnologyViennaAustria

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