Advertisement

Autonomous Robots

, Volume 18, Issue 1, pp 81–102 | Cite as

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

  • R. Manduchi
  • A. Castano
  • A. Talukder
  • L. Matthies
Article

Abstract

Autonomous navigation in cross-country environments presents many new challenges with respect to more traditional, urban environments. The lack of highly structured components in the scene complicates the design of even basic functionalities such as obstacle detection. In addition to the geometric description of the scene, terrain typing is also an important component of the perceptual system. Recognizing the different classes of terrain and obstacles enables the path planner to choose the most efficient route toward the desired goal.

This paper presents new sensor processing algorithms that are suitable for cross-country autonomous navigation. We consider two sensor systems that complement each other in an ideal sensor suite: a color stereo camera, and a single axis ladar. We propose an obstacle detection technique, based on stereo range measurements, that does not rely on typical structural assumption on the scene (such as the presence of a visible ground plane); a color-based classification system to label the detected obstacles according to a set of terrain classes; and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles (such as tree trunks or rocks), even when such obstacles are partially hidden in the grass. These algorithms have been developed and implemented by the Jet Propulsion Laboratory (JPL) as part of its involvement in a number of projects sponsored by the US Department of Defense, and have enabled safe autonomous navigation in high-vegetated, off-road terrain.

obstacle detection terrain classification color classification ladar classification autonomous navigation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abedin, M.N. et al. 2003. Multicolor focal plan array detector technology: A review. In SPIE Int. Symp. on Optical Science and Technology, San Diego.Google Scholar
  2. Adams, M. 2000. Lidar design, use, and calibration concepts for correct environmental detection. IEEE Trans. Robotics Automat., 16:753–761.Google Scholar
  3. Badal, S., Ravela, S., Draper, B., and Hanson, A. 1994. A practical obstacle detection and avoidance system. In 2nd IEEE Workshop on Application of Computer Vision.Google Scholar
  4. Batavia, P. and Singh, S. 2002. Obstacle detection in smooth high curvature terrain. In Proceedings of the IEEE Conference on Robotics and Automation (ICRA '02).Google Scholar
  5. Baten, S., Mandelbaum, R., Luetzeler, M., Burt, P., and Dickmanns, E. 1998. Techniques for autonomous, off-road navigation. IEEE Intelligent Systems Magazine, 57–65.Google Scholar
  6. Bellutta, P., Manduchi, R., Matthies, L., Owens, K., and Rankin, A. 2000. Terrain perception for Demo III". Intelligent Vehicles Conference.Google Scholar
  7. Bergh, C., Kennedy, B., Matthies, L., and Johnson, A. 2000. A compact and lowpower two-axis scanning laser rangefinder for mobile robots. In Seventh Mechatronics Forum International Conference, Atlanta, Georgia.Google Scholar
  8. Bishop, C.M. 1995. Neural Networks for Pattern Recognition. Oxford University Press.Google Scholar
  9. Broggi, A., Bertozzi, M., Fascioli, A., Guarino Lo Bianco, C., and Piazzi, A. 2000. Visual perception of obstacles and vehicles for platooning. IEEE Trans. Intell. Transport. Sys., 1(3).Google Scholar
  10. Buluswar, S.D. and Draper, B.A. 2002. Color models for out-door machine vision. Computer Vision and Image Understanding, 85(2):71–99.Google Scholar
  11. Castano, A., and Matthies, L. 2003. Foliage discrimination using a rotating Ladar. In Proc. IEEE Intl. Conf. Robotics and Automation, Taipei, Taiwan.Google Scholar
  12. Castano, R., Manduchi, R., and Fox, J. 2001.Classification experiments on real-world textures. Workshop on Empirical Evaluation in Computer Vision, Kauai, HI.Google Scholar
  13. Elachi, C. 1987. Introduction to the Physics and Techniques of Remote Sensing. John Wiley and SonsGoogle Scholar
  14. Healey, G. 1992. Segmenting images using normalized color. IEEE Transactions on Systems, Man, and Cybernetics, 22(1):64–73.Google Scholar
  15. Hebert, M., Vandapel, N., Keller, S., and Donamukkala, R.R. 2002. Evaluation and comparison of terrain classification techniques from LADAR data for autonomous navigation. Army Science Conference, Orlando.Google Scholar
  16. Hong, T., Abrams, M., Chang, T., and Shneier, M.O. 2000. An intelligent world model for autonomous off-road driving. Computer Vision and Image Understanding Google Scholar
  17. Hook, S. ASTER Spectral Library. http://speclib.jpl.nasa.gov.Google Scholar
  18. Horn, B.K.P. 1986. Robot Vision.MIT Press: Cambridge, Massachusetts.Google Scholar
  19. Huang, J., Lee, A.B., and Mumford, D. 2000. Statistics of range images. In IEEE Conf. Computer Vision and Pattern Recognition, Hilton Head.Google Scholar
  20. Lacaze, A., Moscovitz, Y., DeClaris, N., and Murphy, K. 1998. Path planning for autonomous vehicles driving over rough terrain. In IEEE ISIC/CIRA/ISAS Joint Conference.Google Scholar
  21. Lacaze, A., Murphy, K., and DelGiorno, M. 2002. Autonomous mobility for the DEMO III Experimental Unmanned Vehicle. Association for Unmanned Vehicle Systems-Unmanned Vehicle.Google Scholar
  22. Leung T. and Malik, J. 1997. On perpendicular texture or: Why do we see more flowers in the distance? In IEEE Conf. Computer Vision and Pattern Recognition, San Juan, Puerto Rico.Google Scholar
  23. Macedo, J., Manduchi, R., and Matthies, L. 2000. Ladar-based discrimination of grass from obstacles for autonomous navigation. In Proc. Intl. Symposium on Experimental Robotics, pp.111–120Google Scholar
  24. Maloney, L.T. and Wandell, B.A. 1986. Color constancy: A method for recovering surface spectral reflectance. J. Opt. Soc. Amer. A, 3:29–33.Google Scholar
  25. Manduchi, R. 2004. Outdoor color classification using only one training image. In European Conference on Computer Vision 2004, Prague.Google Scholar
  26. Matthies, L., Xiong, Y., Hogg, R., Zhu, D., Rankin, A., and Kennedy, B. 2000. A portable, autonomous, urban reconnaissance robot. In International Conference on Intelligent Autonomous Systems, Venice, Italy.Google Scholar
  27. Matthies, L. and Grandjean, P. 1994. Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors. IEEE Transactions on Robotics and Automation, Special Issue on Perception-based Real World Navigation, 10(6).Google Scholar
  28. Matthies, L., Kelly, A., Litwin, T., and Tharp, G. 1996. Obstacle detection for unmanned ground vehicles: a progress report. Robotics Research 7, Springer-Verlag.Google Scholar
  29. Matthies, L., Litwin, T., Owens, K., Rankin, A., Murphy, K., Coombs, D., Gilsinn, J., Hong, T., Legowik, S., Nashman, M., and Yoshimi, B. 1998. Performance evaluation of UGV obstacle detection with CCD/FLIR stereo vision and LADAR. In IEEE ISIC/CIRA/ISAS Joint Conference.Google Scholar
  30. McCartney, E.J. 1976. Optics of the Atmosphere: Scattering by Molecules and Particles. John Wiley and Sons: New York.Google Scholar
  31. McLachlan, G. and Peel, D. 2000. Finite Mixture Models. John Wiley and Sons.Google Scholar
  32. Mehlhorn, K. 1984. Data Structures and Efficient Algorithms. Springer Verlag.Google Scholar
  33. Nayar, S.K., and Narasimhan, S.G. 2000. Vision in bad weather. In IEEE Conf. Computer Vision and Pattern Recognition.Google Scholar
  34. Rankin, A., Owens, K., Matthies, L., and Litwin, T. 1998. Terrain-adaptive gaze and velocity control for ugv obstacle detection. In Association for Unmanned Vehicle Systems International Annual Symposium.Google Scholar
  35. Ripley, B. 1996. Pattern Recognition and Neural Networks. Cambridge University Press.Google Scholar
  36. Roberts, D., Smith, M., and Adams, J. 1993. Green vegetation, non-photosynthetic vegetation, and soils in AVIRIS data. Remote Sens. Environ., 44:255–269.Google Scholar
  37. Shoemaker, C.M. and Bornstein, J.A. 1998. The Demo III UGV program: A testbed for autonomous navigation Research. In IEEE International Symposium on Intelligent Control, Gaithersburg, MD.Google Scholar
  38. Shi, X. and Manduchi, R. 2003. A study on Bayes feature fusion for image classification. In IEEE Workshop on Statistical Analysis in Computer Vision,Madison, WI.Google Scholar
  39. Singh, S., and Keller, P. 1991. Obstacle detection for high speed autonomous navigation. In IEEE Int. Conference on Robotics and Automation, Sacramento, CA, pp. 2798–2805.Google Scholar
  40. Talukder, A., Manduchi, R., Castano, R., Owens, K., Matthies, L., Castano, A., and Hogg, R. 2002. Autonomous terrain characterization and modeling for dynamic control of unmanned vehicles. In IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland.Google Scholar
  41. Talukder, A., Manduchi, R., Rankin, A., and Matthies, L. 2002. Fast and reliable obstacle detection and segmentation for cross-country navigation. In IEEE Intelligent Vehicles Symposium,Versailles, France.Google Scholar
  42. Williamson, T. and Thorpe, C. 1998. A specialized multibaseline stereo technique for obstacle detection. In IEEE Conf. Computer Vision and Pattern Recognition, pp. 238–244.Google Scholar
  43. Wolff, L. 1996. Polarization phased-based method for material classification and object recognition in computer vision. In IEEE Conf. Computer Vision and Pattern Recognition.Google Scholar
  44. Zhang, Z., Weiss, R., and Hanson, A.R. 1994. Qualitative obstacle detection. In IEEE Conf. Computer Vision and Pattern Recognition, pp. 554–559.Google Scholar

Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  • R. Manduchi
    • 1
  • A. Castano
    • 2
  • A. Talukder
    • 2
  • L. Matthies
    • 2
  1. 1.University of California at Santa CruzSanta Cruz, CAUSA
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, CAUSA

Personalised recommendations