Active/dynamic stereo for navigation
Abstract
Stereo vision and motion analysis have been frequently used to infer scene structure and to control the movement of a mobile vehicle or a robot arm. Unfortunately, when considered separately, these methods present intrinsic difficulties and a simple fusion of the respective results has been proved to be insufficient in practice.
The paper presents a cooperative schema in which the binocular disparity is computed for corresponding points in several stereo frames and it is used, together with optical flow, to compute the time-to-impact. The formulation of the problem takes into account translation of the stereo set-up and rotation of the cameras while tracking an environmental point and performing one degree of freedom active vergence control. Experiments on a stereo sequence from a real scene are presented and discussed.
Keywords
Optical Flow Image Point Stereo Vision Relative Depth Stereo PairReferences
- [Bro86]R.A. Brooks. A robust layered control system for a mobile robot. IEEE Trans. on Robotics and Automat., RA-2:14–23, April 1986.Google Scholar
- [CGS91]G. Casalino, G. Germano, and G. Sandini. Tracking with a robot head. In Proc. of ESA Workshop on Computer Vision and Image Processing for Spaceborn Applications, Noordwijk, June 10–12, 1991.Google Scholar
- [FGMS90]F. Ferrari, E. Grosso, M. Magrassi, and G. Sandini. A stereo vision system for real time obstacle avoidance in unknown environment. In Proc. of Intl. Workshop on Intelligent Robots and Systems, Tokyo, Japan, July 1990. IEEE Computer Society.Google Scholar
- [GST89]E. Grosso, G. Sandini, and M. Tistarelli. 3d object reconstruction using stereo and motion. IEEE Trans. on Syst. Man and Cybern., SMC-19, No. 6, November/December 1989.Google Scholar
- [HS81]B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17 No.1–3:185–204, 1981.Google Scholar
- [KP86]B. Kamgar-Parsi. Practical computation of pan and tilt angles in stereo. Technical Report CS-TR-1640, University of Maryland, College Park, MD, March 1986.Google Scholar
- [LD88]L. Li and J.H. Duncan. Recovering three-dimensional translational velocity and establishing stereo correspondence from binocular image flows. Technical Report CS-TR-2041, University of Maryland, College Park, MD, May 1988.Google Scholar
- [Mut86]K.M. Mutch. Determining object translation information using stereoscopic motion. IEEE Trans. on PAMI-8, No. 6, 1986.Google Scholar
- [OC90]T.J. Olson and D.J. Coombs. Real-time vergence control for binocular robots. Technical Report 348, University of Rochester — Dept. of Computer Science, 1990.Google Scholar
- [TGS91]M. Tistarelli, E. Grosso, and G. Sandini. Dynamic stereo in visual navigation. In Proc. of Int. Conf. on Computer Vision and Pattern Recognition, Lahaina, Maui, Hawaii, June 1991.Google Scholar
- [TK91]C. Tomasi and T. Kanade. Shape and motion from image streams: a factorization method. Technical Report CS-91-105, Carnegie Mellon University, Pittsburgh, PA, January 1991.Google Scholar
- [TS90]M. Tistarelli and G. Sandini. Estimation of depth from motion using an anthropomorphic visual sensor. Image and Vision Computing, 8, No. 4:271–278, 1990.Google Scholar
- [UGVT88]S. Uras, F. Girosi, A. Verri, and V. Torre. Computational approach to motion perception. Biological Cybernetics, 1988.Google Scholar
- [WD86]A.M. Waxman and J.H. Duncan. Binocular image flows: Steps toward stereo-motion fusion. IEEE Trans. on PAMI — 8, No. 6, 1986.Google Scholar