Outdoor Mapping and Navigation Using Stereo Vision

  • Kurt Konolige
  • Motilal Agrawal
  • Robert C. Bolles
  • Cregg Cowan
  • Martin Fischler
  • Brian Gerkey
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 39)


We consider the problem of autonomous navigation in an unstructured outdoor environment. The goal is for a small outdoor robot to come into a new area, learn about and map its environment, and move to a given goal at modest speeds (1 m/s). This problem is especially difficult in outdoor, off-road environments, where tall grass, shadows, deadfall, and other obstacles predominate. Not surprisingly, the biggest challenge is acquiring and using a reliable map of the new area. Although work in outdoor navigation has preferentially used laser rangefinders [14,2,6], we use stereo vision as the main sensor. Vision sensors allow us to use more distant objects as landmarks for navigation, and to learn and use color and texture models of the environment, in looking further ahead than is possible with range sensors alone.


Ground Plane Stereo Vision Obstacle Detection Global Planning Visual Odometry 
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.
    Agrawal, M., Konolige, K.: Real-time localization in outdoor environments using stereo vision and inexpensive gps. In: ICPR. Intl. Conf. of Pattern Recognition (to appear)Google Scholar
  2. 2.
    Bellutta, P., Manduchi, R., Matthies, L., Owens, K., Rankin, A.: Terrain perception for DEMO III. In: Proc. of the IEEE Intelligent Vehicles Symp. (2000)Google Scholar
  3. 3.
    Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robotics and Automation Magazine 4(1), 23–33 (1997)CrossRefGoogle Scholar
  5. 5.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. of Computer and System Sciences 55(1), 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Guivant, J., Nebot, E., Baiker, S.: High accuracy navigation using laser range sensors in outdoor applications. In: ICRA (2000)Google Scholar
  7. 7.
    Gutmann, J.S., Konolige, K.: Incremental mapping of large cyclic environments. In: CIRA (1999)Google Scholar
  8. 8.
    Iagnemma, K., Genot, F., Dubowsky, S.: Rapid physics-based rough-terrain rover planning with sensor and control uncertainty. In: ICRA (1999)Google Scholar
  9. 9.
    Kelly, A.: An intelligent predictive controller for autonomous vehicles. Technical Report CMU-RI-TR-94-20, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania (May 1994)Google Scholar
  10. 10.
    Konolige, K.: Small vision systems: hardware and implementation. In: Intl. Symp. on Robotics Research, pp. 111–116 (1997)Google Scholar
  11. 11.
    Konolige, K.: A gradient method for realtime robot control. In: IROS (2000)Google Scholar
  12. 12.
    Leonard, J.J., Newman, P.: Consistent, convergent, and constant-time slam. In: IJCAI (2003)Google Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Intl. J. of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Montemerlo, M., Thrun, S.: Large-scale robotic 3-d mapping of urban structures. In: ISER (2004)Google Scholar
  15. 15.
    Moravec, H., Elfes, A.: High resolution maps for wide angles sonar. In: ICRA (1985)Google Scholar
  16. 16.
    Nister, D., Naroditsky, O., Bergen, J.: Visual odometry. In: CVPR (2004)Google Scholar
  17. 17.
    Olson, C.F., Matthies, L.H., Schoppers, M., Maimone, M.W.: Robust stereo ego-motion for long distance navigation. In: CVPR (2000)Google Scholar
  18. 18.
    Philippsen, R., Siegwart, R.: An interpolated dynamic navigation function. In: ICRA (2005)Google Scholar
  19. 19.
    Rankin, A., Huertas, A., Matthies, L.: Evaluation of stereo vision obstacle detection algorithms for off-road autonomous navigation. In: AUVSI Symp. on Unmanned Systems (2005)Google Scholar
  20. 20.
    Spero, D.J., Jarvis, R.A.: 3D vision for large-scale outdoor environments. In: ACRA. Proc. of the Australasian Conf. on Robotics and Automation (2002)Google Scholar
  21. 21.
    Stentz, A.: Optimal and efficient path planning for partially-known environments. In: ICRA, vol. 4, pp. 3310–3317 (1994)Google Scholar
  22. 22.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. 511–518 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kurt Konolige
    • 1
  • Motilal Agrawal
    • 1
  • Robert C. Bolles
    • 1
  • Cregg Cowan
    • 1
  • Martin Fischler
    • 1
  • Brian Gerkey
    • 1
  1. 1.Artificial Intelligence Center, SRI International 

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