Off-road Terrain Mapping Based on Dense Hierarchical Real-Time Stereo Vision

  • Thomas Kadiofsky
  • Johann Weichselbaum
  • Christian Zinner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7431)


We present a robust and fast method for on-line creation of local maps based on stereo vision for vehicles operating in off-road environments. A 3D vision sensor system with a high accuracy even at long ranges and wide field of view is used as the only sensor input. Due to the hierarchical mode of operation, dense stereo matching on image resolution 1200 ×525 and a disparity range of 280 becomes feasible at 10 fps. Beside of an achieved speedup factor of 6.47, a significant increase in the density of resulting disparity maps on real-world scenes has been achieved. Multiple captured views are aligned and integrated into a probabilistic elevation map suited for modeling dynamic environments. Efficient computations in the u-disparity-space and a stereo sensor model are at the core of the iterative update process.


stereo vision terrain mapping real-time elevation map 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Humenberger, M., Zinner, C., Weber, M., Kubinger, W., Vincze, M.: A fast stereo matching algorithm suitable for embedded real-time systems. Computer Vision and Image Understanding 114, 1180–1202 (2009)CrossRefGoogle Scholar
  3. 3.
    De-Maeztu, L., Mattoccia, S., Villanueva, A., Cabeza, R.: Linear stereo matching. In: Proc. International Conference on Computer Vision, ICCV 2011. IEEE (2011)Google Scholar
  4. 4.
    Rhemann, C., Hosni, A., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. In: IEEE Computer Vision and Pattern Recognition, CVPR (2011)Google Scholar
  5. 5.
    Nister, D., Naroditsky, O., Bergen, J.: Visual odometry. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–659 (2004)Google Scholar
  6. 6.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 239–256 (1992)Google Scholar
  7. 7.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: Third International Conference on 3D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
  8. 8.
    Elfes, A.: Occupancy grid: A stochastic spatial representation for active robot perception. In: 6th Conference on Uncertainty in Artificial Intelligence, pp. 136–146 (1990)Google Scholar
  9. 9.
    Murray, D., Little, J.: Using real-time stereo vision for mobile robot navigation. Autonomous Robots 8, 161–171 (2000)CrossRefGoogle Scholar
  10. 10.
    Perrollaz, M., Yoder, J.D., Spalanzani, A., Laugier, C.: Using the disparity space to compute occupancy grids from stereo-vision. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2721–2726 (2010)Google Scholar
  11. 11.
    Kweon, I.S., Kanade, T.: High-resolution terrain map from multiple sensor data. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 278–292 (1992)CrossRefGoogle Scholar
  12. 12.
    Pfaff, P., Triebel, R., Burgard, W.: An efficient extension to elevation maps for outdoor terrain mapping and loop closing. International Journal of Robotics Research 26, 217–230 (2007)CrossRefGoogle Scholar
  13. 13.
    Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems. In: IEEE International Conference on Robotics and Automation (2010)Google Scholar
  14. 14.
    Zinner, C., Humenberger, M., Ambrosch, K., Kubinger, W.: An Optimized Software-Based Implementation of a Census-Based Stereo Matching Algorithm. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 216–227. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Hung, Y., Chen, C., Hung, K., Chen, Y., Fuh, C.: Multipass hierarchical stereo matching for generation of digital terrain models from aerial images. In: Machine Vision Applications, pp. 280–291. Springer (1998)Google Scholar
  16. 16.
    Sun, C.: A fast stereo matching method. In: Digital Image Computing: Techniques and Applications (1997)Google Scholar
  17. 17.
    Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. In: 1991 IEEE International Conference on Robotics and Automation, pp. 2724–2729 (1991)Google Scholar
  18. 18.
    Blais, G., Levine, M.D.: Registering multiview range data to create 3d computer objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 820–824 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Thomas Kadiofsky
    • 1
  • Johann Weichselbaum
    • 1
  • Christian Zinner
    • 1
  1. 1.AIT Austrian Institute of Technology GmbHViennaAustria

Personalised recommendations