A Vision Driven Automatic Assembly Unit

  • Gernot Bachler
  • Martin Berger
  • Reinhard Röhrer
  • Stefan Scherer
  • Axel Pinz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)


The development of a flexible assembly unit is one of the de- manding tasks in industrial manufacturing. A higher degree of flexibility is mostly payed by an increasing complexity of the involved hardware. In this paper we present a three-step concept for a vision driven automatic assembly unit. These three steps are robust bin-picking to isolate objects from a pile of unorganized parts, exact pose determination to enable industrial mounting and visual inspection of the final assembling. For robust bin-picking we present a new structured light approach. Experiments show the robust and accurate behavior of the proposed algorithm and motivate the implementation in an industrial system. For exact pose determination, the second step, a pose estimation based on a modified view based approach, followed by a model based refinement is proposed. Initial experiments promise a fast and exact pose determination.


Plane Detection Stereoscopic Image Plane Representation Assembly Unit Vacuum Sucker 
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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  1. 1.
    Ezzet Al-Hujazi and Arun Sood. Range Image Segmentation with Applications to Robot Bin-Picking Using a Vacuum Gripper. IEEE Transactions on Systems Man and Cybernetics, 20(6):1313–1324, November/December 1990.CrossRefGoogle Scholar
  2. 2.
    Z. Chen, S.Y. Ho, and D.C. Tseng. Polyhedral Face Reconstruction and Modeling from a Single Image with Structured Light. IEEE Transactions on Systems Man and Cybernetics, 23(3):864–872, May/June 1993.CrossRefGoogle Scholar
  3. 3.
    Steven Gold, Anand Rangarajan, Chien-Ping Lu, and Suguna Pappu. New Algorithms For 2D and 3D Point Matching: Pose Estimation and Correspondence. Pattern Recognition, 31(8):1019–1031, August 1998.CrossRefGoogle Scholar
  4. 4.
    R. M. Haralick, H. Joo, C. Lee, X. Zhuang, V. G. Vaidya, and M. B. Kim. Pose Estimation from Corresponding Point Data. IEEE Transactions on Systems Man and Cybernetics, 19(6):1426–1446, November-December 1989.CrossRefGoogle Scholar
  5. 5.
    Gongzhu Hu and George Stockman. 3-D Surface Solution Using Structured Light and Constraint Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(4):390–402, April 1989.CrossRefGoogle Scholar
  6. 6.
    David G. Lowe. Three-Dimensional Object Recognition from Single Two-Dimensional Images. Artificial Intelligence, 31(3):355–395, March 1987.CrossRefGoogle Scholar
  7. 7.
    Hiroshi Murase and Shree K. Nayar. Visual Learning and Recognition of 3-D Objects from Appearance. International Journal of Computer Vision, 14:5–24, 1995.CrossRefGoogle Scholar
  8. 8.
    Krisnawan Rahardja and Akio Kosaka. Vision-Based Bin-Picking: Recognition and Localization of Multiple Comlex Objects Using Simple Visual Cues. In Proceedings of the International Conference on Intelligent Robotics and Systems, Osaka, Japan, November 1996. IEEE / RSJ.Google Scholar
  9. 9.
    Martin Rutishauser and Frank Ade. From Vision to Action: Grasping Unmodeled Objects from a Heap. In Intelligent Robots and Computer Vision XIV, SPIE’s Photonic East Symposium, volume 2588, pages 375–386. The international Society for Optical Engineering, October 1995.Google Scholar
  10. 10.
    J. Salvi, J. Batlle, and E. Mouaddib. A robust-coded pattern projection for dynamic 3D scene measurement. Pattern Recognition Letters, 19(11):1055–1065, September 1998.CrossRefGoogle Scholar
  11. 11.
    N. Shrikhande and G. Stockman. Surface Orientation from a Projected Grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):650–655, June 1989.CrossRefGoogle Scholar
  12. 12.
    Marjan Trobina and Aleš Leonardis. Grasping Arbitrarily Shaped 3-D Objects from a Pile. In International Conference on Robotics and Automation, volume 1, pages 241–246. IEEE, May 1995.Google Scholar
  13. 13.
    R.J. Valkenburg and A.M. McIvor. Accurate 3D measurement using a structured light system. Image and Vision Computing, 16:99–110, 1998.CrossRefGoogle Scholar
  14. 14.
    Y.F. Wang, A. Mitiche, and J.K. Aggarwal. Computation of Surface Orientation and Structure of Objects Using Grid Coding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(1):129–137, January 1987.CrossRefGoogle Scholar
  15. 15.
    P. Wunsch and G. Hirzinger. Registration of CAD-Models to Images by Iterative Inverse Perspective Matching. In Proc. International Conference on Pattern Recognition, pages 78–83, 1996.Google Scholar
  16. 16.
    Billibon H. Yoshimi and Peter Allen. Closed-Loop Visual Grasping and Manipulation. In Proceedings of Image Understanding Workshop, pages 1353–1360, Palm Springs, California, February 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Gernot Bachler
    • 1
  • Martin Berger
    • 1
  • Reinhard Röhrer
    • 1
  • Stefan Scherer
    • 1
  • Axel Pinz
    • 1
  1. 1.Department for Computer Graphics and VisionGraz University of TechnologyGrazAustria

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