Abstract
This paper can be divided into two main parts. Both of them describe one of those parts of an intelligent robot control system, which makes the system capable to interact with its environment via visual information. The two parts can be handled as complementary parts of each other. The first part shows the solution of the data extraction from the visual information. This is a well known machine vision problem, in this case, the applied method is a passive stereo type. The second part explains the method of visual representation of the data. This is a visualization problem, in this case, a virtual reality system is used. With the use of the detailed graphic models of VR, efficient off-line robot programming and simulation is available.
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Avidan, S. and Shashua, A.: Tensor embedding of the fundamental matrix, Post-ECCV SMILE Workshop, Institute of Computer Science, Frieburg, Germany, June 1998, Lecture Notes in Comput. Sci., Vol. 1506, Springer, Berlin. Internet: http://www.cs.huji.ac.il/~shashua/.
Bejczy, A. K.: Calibrated virtual reality in telerobotics, California Institute of Technology, Pasadena, CA, USA, 1996.
Boufama, B., Mohr, R., and Veillon, F.: Euclidean constraints for uncalibrated reconstruction, in: Proc. of the 4th Internat. Conf. on Computer Vision, Berlin, Germany, May 1993, pp. 466–470. Internet: http://www.inrialpes.fr/movi/pub/Publications/en/par_annee.html.
Csurka, G., Demirdjian, D., and Horau, R.: Finding the collineation between two projective reconstructions, INRIA Research Report No. 3468, August 1998. Internet: http://www. inrialpes.fr/movi/people/Horaud/Radu-publications.html.
Devernay, F. and Faugeras, O. D.: From projective to Euclidean reconstruction, INRIA, France, CVPR '96. Internet: ftp://ftp.inria.fr/INRIA/tech-reports/RR/RR-2725.ps.gz.
Faugeras, O. D.: Three-Dimensional Computer Vision (A Geometric Viewpoint), MIT Press, Cambridge, MA, 1992, pp. 62–63.
Faugeras, O. D.: What can be seen in three dimensions with an uncalibrated stereo rig, in: Proc. of the 2nd European Conf. on Computer Vision, G. Sandini (ed.), Springer, Santa Margherita ligure, Italy, 1992, pp. 563–578.
Glassner, A. S.: Graphics Gems I, Academic Press, New York, 1990.
Gottschalk, S., Lin, M. C., and Manocha, D.: OBB tree: A hierarchical structure for rapid interference detection, in: Proc. of the 1996 SIGGRAPH Conf., University of North Carolina, Chapel Hill, 1996. Internet: http://www.cs.unc.edu/~lin/papers.html.
Hartley, I. R.: Projective reconstruction from line correspondences, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1994, pp. 903–907.
Hartley, I. R.: Lines and points in three views and the trifocal tensor, Internat. J. Computer Vision 22(2) (1997), 125–140.
Jonathan, D., Lin, M. C., Manocha, D., and Ponamgi, M. K.: I-COLLIDE: An Interactive and Exact Collision Detection System, University of North Carolina, Chapel Hill, 1995. Internet: http://haptic.mech.nwu.edu/library/cohenj/acm95
Lantos, B.: 3D Képfeldolgozási módszerek, manuscript, Technical University of Budapest, 1994 (in Hungarian).
Lantos, B.: Some possibilities to increase the intelligence in the development and application of robots, in: Proc. of the INES '98, Vienna, Austria, 1998, pp. 7–18.
Lantos, B., Klatsmányi, P., Ludvig, L., and Tél, F.: Intelligent control system of a robot with dextrous hand, in: Proc. of the IEEE Internat. Conf. on Intelligent Engineering Systems INES'97, Budapest, 1997, pp. 129–134.
Luong, Q. T. and Faugeras, O. D.: The fundamental matrix: Theory, algorithms and stability analysis, Internat. J. Computer Vision 17(1) (1995), 43–76.
Maekawa, H. and Hollerbach, J. M.: Haptic display for object grasping and manipulating in virtual environment, in: Proc. of the IEEE Internat. Conf. on Robotics and Automation, Leuven, Belgium, 1998. Internet: http://www.cs.utah.edu/~jmh/Recent.html.
Mohr, R., Quan, L., and Veillon, F.: Relative 3D reconstruction using multiple uncalibrated images, Internat. J. Robotics Res. 14(6) (1995), 619–632.
Pope, A. R.: Learning to recognize objects in images: Acquiring and using probabilistic models of appearance, PhD Thesis, The University of British Columbia, 1995.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T.: Numerical Recipes in C, Cambridge Univ. Press, Cambridge, 1988, pp. 681–688.
Shashua, A.: The fundamental construct of multiple view geometry and its applications, Institute of Computer Science, The Hebrew University, 1997. Internet: http://www.cs.huji.ac. il/~shashua/.
Shashua, A. and Werman, M.: Fundamental tensor: On the geometry of three perspective views, Institute of Computer Science, The Hebrew University, 1995. Internet: http://www. cs.huji.ac.il/labs/vision/biblo.html.
Torr, P. H. S. and Zisserman, A.: Robust parametrization and computation of the trifocal tensor, Image Vision Computing 15 (1997), 591–605. Internet: http://imogen.robots. ox.ac.uk:20000/~geoff/papers.cgi.
Welch, G. and Bishop, G.: An Introduction to the Kalman Filter, University of North Carolina, Chapel Hill, 1997. Internet: http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html.
Wells, W. M.: Statistical object recognition, PhD Thesis, MIT Press, Cambridge, MA, 1993.
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Tél, F., Tóth, E. Stereo Image Processing and Virtual Reality in an Intelligent Robot Control System. Journal of Intelligent and Robotic Systems 27, 113–134 (2000). https://doi.org/10.1023/A:1008154214621
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DOI: https://doi.org/10.1023/A:1008154214621