Abstract
The study of visual navigation problems requires the integration of visual processes with motor control. Most essential in approaching this integration is the study of appropriate spatio-temporal representations which the system computes from the imagery and which serve as interfaces to all motor activities. Since representations resulting from exact quantitative reconstruction have turned out to be very hard to obtain, we argue here for the necessity of representations which can be computed easily, reliably and in real time and which recover only the information about the 3D world which is really needed in order to solve the navigational problems at hand. In this paper we introduce a number of such representations capturing aspects of 3D motion and scene structure which are used for the solution of navigational problems implemented in visual servo systems. In particular, the following three problems are addressed: (a) to change the robot's direction of motion towards a fixed direction, (b) to pursue a moving target while keeping a certain distance from the target, and (c) to follow a wall-like perimeter. The importance of the introduced representations lies in the following:
-
They can be extracted using minimal visual information, in particular the sign of flow measurements or the the first order spatiotemporal derivatives of the image intensity function. In that sense they are direct representations needing no intermediate level of computation such as correspondence.
-
They are global in the sense that they represent how three-dimensional information is globally encoded in them. Thus, they are robust representations since local errors do not affect them.
-
Usually, from sequences of images, three-dimensional quantities such as motion and shape are computed and used as input to control processes. The representations discussed here are given directly as input to the control procedures, thus resulting in a real time solution.
The support of the Office of Naval Research under Contract N00014-93-1-0257, National Science Foundation under Grant IRI-90-57934 and the Austrian ”Fonds zur Förderung der wissenschaftlichen Forschung” project No S7003 and a postgraduate scholarship from Tan Kah Khee Foundation is gratefully acknowledged.
Chapter PDF
Keywords
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.
References
Aloimonos, J. (Y.): Purposive and qualitative active vision. Proc. DARPA Image Understanding Workshop (1990) 816–828
J. Aloimonos, I. Weiss, and A. Bandopadhay, Active vision. International Journal of Computer Vision, 2:333–356, 1988.
R.C. Arkin, R. Murphy, M. Pearson and D. Vaughn, Mobile robot docking operations in a manufacturing environment: Progress in visual perceptual strategies. In Proc. IEEE International Workshop on Intelligent Robots and Systems, pages 147–154, 1989.
R. Bajcsy, Active perception. Proc. of the IEEE, 76:996–1005, 1988.
D. Ballard and C. Brown, Principles of animate vision. CVGIP: Image Understanding, 45:3–21, Special Issue on Purposive, Qualitative, Active Vision, Y. Aloimonos (Ed.), 1992.
B. Espiau, F. Chaumette, and P. Rives, A new approach to visual servoing in robotics. IEEE Trans. on Robotics and Automation, 8:313–326, 1992.
C. Fermüller, L.F. Cheong and Y. Aloimonos, 3D Motion and Shape Representations in Visual Servo Control. Technical Report, Center for Automation Research, University of Maryland, CAR-TR-799, July 1995.
C. Fermüller and Y. Aloimonos, Tracking facilitates 3-D motion estimation. Biological Cybernetics, 67:147–158, 1992.
C. Fermüller and Y. Aloimonos, Direct perception of three-dimensional motion through patterns of visual motion. Science, 270:1973–1976, 1995.
C. Fermüller and Y. Aloimonos, On the geometry of visual correspondence. International Journal of Computer Vision, to appear, 1995.
E. Francois and P. Bouthemy, Derivation of qualitative information in motion analysis. Image and Vision Computing, 8:279–288, 1990.
R.C. Nelson and Y. Aloimonos, Obstacle avoidance using flow field divergence. IEEE Trans. on Pattern Analysts and Machine Intelligence, 11:1102–1106, 1989.
D. Raviv and M. Herman, Visual Servoing from 2D image cues. In Y. Aloimonos (Ed.), Active Perception, Advances in Computer Vision, pages 191–229, Lawrence Erlbaum, Hillsdale, NJ, 1993.
J. Santos-Victor, G. Sandini, F. Curotto and S. Garibaldi, Divergent stereo for robot navigation: Learning from bees. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 434–439, 1993.
G. Sandini, F. Gandolfo, E. Grosso and M. Tistarelli, Vision during action. In Y. Aloimonos (Ed.), Active Perception, Advances in Computer Vision, pages 151–190. Lawrence Erlbaum, Hillsdale, NJ, 1993.
S.B. Skaar, W.H. Brockman, and R. Hanson, Camera-space manipulation. International Journal of Robotics Research, 6:20–32, 1987.
M. Subbarao, Bounds on time-to-collision and rotational component from first-order derivatives of image flow. Computer Vision, Graphics, and Image Processing, 50:329–341, 1990.
L.E Weiss and A.C. Sanderson, Dynamic sensor-based control of robots with visual feedback. IEEE Trans. on Robotics and Automation, 3:404–417, 1987.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheong, L., Fermüller, C., Aloimonos, Y. (1996). Spatiotemporal representations for visual navigation. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015577
Download citation
DOI: https://doi.org/10.1007/BFb0015577
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61122-6
Online ISBN: 978-3-540-49949-7
eBook Packages: Springer Book Archive