Skip to main content
SpringerLink
Log in

Using Real-Time Stereo Vision for Mobile Robot Navigation

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper describes a working vision-based mobile robot that navigates and autonomously explores its environment while building occupancy grid maps of the environment. We present a method for reducing stereo vision disparity images to two-dimensional map information. Stereo vision has several attributes that set it apart from other sensors more commonly used for occupancy grid mapping. We discuss these attributes, the errors that some of them create, and how to overcome them. We reduce errors by segmenting disparity images based on continuous disparity surfaces to reject “spikes” caused by stereo mismatches. Stereo vision processing and map updates are done at 5 Hz and the robot moves at speeds of 300 cm/s.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Barraquand, J., Langlois, B., and Latombe, J.1992.Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, Cybernetics,22(2):224–241.

    Google Scholar 

  • Brooks, R.A.1987.A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2: 14–23.

    Google Scholar 

  • Brooks, R.A., Connell, J., and Ning, P.1988.Herbert: Asecond generation mobile robot. A.I. Memo No. 1016, MIT AI Laboratory.

  • Dudek, G., Jenkin, M., Milios, E., and Wilkes, D. 1997a. On building and navigating with a local metric environmental map. InICAR-97.

  • Dudek, G., Milios, E., Jenkin, M., and Wilkes, D. 1997b. Map validation and self-location for a robot with a graph-like map. Robotics and Autonomous Systems,22(2)159–178.

    Google Scholar 

  • Elfes, A. 1989. Using occupancy grids for mobile robot perception and navigation. Computer, 22(6): 46–57.

    Article  Google Scholar 

  • Fua, P.1993.A parallel stereo algorithm that produces dense depth maps and preserves image features.Machine Vision and Applications, 26(1):35–49.

    Google Scholar 

  • Kanade, T., Yoshida, A., Oda, K., Kano, H., and Tanaka, M.1996. A video-rate stereo machine and its new applications. In Proc. IEEE Int'l Conf. on Computer Vision and Pattern Recognition, San Francisco, California, USA.

  • Khatib, O. 1986. Real–time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5(1): 90–98.

    Google Scholar 

  • Konolige, K. 1997. Improved occupancy grids for map. Autonomous Robots, (4):351–367.

  • Lengyel, J., Reichert, M., Donald, B., and Greenberg, D.1990.Real– time robot motion planning using rasterizing computer graphics hardware. In Proc. of SIGGRAPH, Dallas, Texas, pp. 327–335.

  • Little, J.J., Jennings, C., and Murray, D.R. 1998a. Vision-based mapping with cooperative robots. In SPIE-98, Sensor Fusion and Decentraed Control in Robotic Systems, pp. 2–12.

  • Little, J.J., Murray, D., and Lu, J. 1998b. Identifying stereo corners for mobile robot locaation. In IROS-98, pp. 1072–1077.

  • Matthies, L. and Grand jean, P.1987.Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors. RA,10(6).

  • Matthies, L. and Shafer, S.A.1987.Error modeling in stereo navigation. IEEE Journal of Robotics and Automation, 3:239–248.

    Google Scholar 

  • Moravec, H. and Elfes, A.1985.High-resolution maps from wideangle sonar. In Proc. IEEE Int'l Conf. on Robotics and Automation, St. Louis, Missouri.

  • Murray, D. and Jennings, C.1997.Stereo vision based mapping for a mobile robot. In Proc. IEEE Conf. on Robotics and Automation,1997.

  • Nickerson, S.B. et al.1993.Ark: Autonomous navigation of a mobile robot in a known environment. In IAS-3, pp. 288–293.

  • Okutomi, M. and Kanade, T.1993.A multiple-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4):353–363.

    Article  Google Scholar 

  • Tsai, R.Y.1987.A versatile camera calibration technique for highaccuracy 3d machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation, 3:323–344.

    Google Scholar 

  • Tucakov, V., Sahota, M., Murray, D., Mackworth, A., Little, J., Kingdon, S., Jennings, C., and Barman, R.1997.Spinoza: A stereoscopic visually guided mobile robot. In Proceedings of the Thirteenth Annual Hawaii International Conference of System Sciences, pp. 188–197.

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of British Columbia, Canada

    Don Murray

  2. Computer Science Department, University of British Columbia, Canada

    James J. Little

Authors
  1. Don Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James J. Little
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Murray, D., Little, J.J. Using Real-Time Stereo Vision for Mobile Robot Navigation. Autonomous Robots 8, 161–171 (2000). https://doi.org/10.1023/A:1008987612352

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008987612352

Search

Navigation