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Perception of Environment Properties Relevant for Off-road Navigation

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Autonome Mobile Systeme 2009

Part of the book series: Informatik aktuell ((INFORMAT))

Abstract

In this paper a set of physical properties is presented that can be utilized for save and efficient navigation in unstructured terrain. This set contains properties of positive obstacles, i.e. flexibility, shape, dimensions, etc. as well as properties of negative obstacles and ground, i.e. slope, carrying capacity, slippage, etc. By means of these properties a classifier is developed that supports the discrimination from traversable to non-traversable areas. Furthermore, an overview of different sensor systems, that can be employed to determine some these properties, is given.

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References

  1. Angelova A., Matthies L., Helmick D. et al.: Learning to predict slip for ground robots. IEEE ICRA, pp.3324–3331, May 15–19 2006.

    Google Scholar 

  2. Armbrust C., Braun T., Föhst T. et al.: RAVON — The Robust Autonomous Vehicle for Off-road Navigation. RISE, January 12–14 2009.

    Google Scholar 

  3. Bergquist U.: Colour Vision and Hue for Autonomous Vehicle Guidance. Master Thesis, University of Linköping, December 1999.

    Google Scholar 

  4. Bradley D. M., Unnikrishnan R., Bagnell J.: Vegetation Detection for Driving in Complex Environments. IEEE ICRA, Roma, Italy, April 10–14 2007.

    Google Scholar 

  5. Braun T., Bitsch H., Berns K.: Visual Terrain Traversability Estimation using a Combined Slope/Elevation Model. KI Conference, pp. 177–184, 2008.

    Google Scholar 

  6. Braun T., Seidler B., Berns K.: Adaptive Visual Terrain Traversability Estimation using Behavior Observation. IARP Workshop on Environmental Maintenance and Protection, July 2008.

    Google Scholar 

  7. Broggi A., Bertè S.: Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach. Journal of Artificial Intelligence Research, Vol. 3, pp. 325–348, 1995.

    Google Scholar 

  8. He Y., Wang H., Zhang B.: Color-based road detection in urban traffic scenes. IEEE ITSS, Vol. 5, pp. 309–318, 2004.

    Google Scholar 

  9. Hong T., Abrams M., Chang T. et al.: An Intelligent World Model for Autonomous Off-Road Driving. Computer Vision and Image Understanding, Hook, S. ASTER Spectral Library, 2000.

    Google Scholar 

  10. Hong T., Rasmussen C., Chang T. et al.: Fusing Ladar and Color Image Information for Mobile Robot Feature Detection and Tracking. IAS, 2002.

    Google Scholar 

  11. Hu M., Yang W., Ren M., Yang J.: A Vision Based Road Detection Algorithm. IEEE RAM, pp. 846–850, December 2004.

    Google Scholar 

  12. Kim D., Sun J., Oh S.M. et al.: Traversability Classification using Unsupervised On-line Visual Learning for Outdoor Robot Navigation. IEEE ICRA, pp. 518–525, May 15–19 2006.

    Google Scholar 

  13. Kuhnert K.-D., Stommel M.: Fusion of Stereo-Camera and PMD-Camera Data for Real-Time Suited Precise 3D Environment Reconstruction, IEEE IROS, pp. 4780–4785, 2006.

    Google Scholar 

  14. Manduchi R., Castano A., Talukder A. et al.: Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation. Journal of Autonomous Robots, vol. 18, pp. 81–102, 2005.

    Article  Google Scholar 

  15. Matthies L., Bellutta P., McHenry M.: Detecting water hazards for autonomous off-road navigation. IEEE IROS, October 27, 2003.

    Google Scholar 

  16. May S., Werner B., Surmann H., Pervölz K.: 3D time-of-flight cameras for mobile robotics. IEEE IROS, pp. 790–795, 2006.

    Google Scholar 

  17. Rankin A., Matthies L., Huertas A.: Daytime Water Detection by Fusing Multiple Cues for Autonomous Off-Road Navigation. 24th Army Science Conference, Orlando, Florida, USA, November 29–December 2 2004.

    Google Scholar 

  18. Schäfer H., Hach A., Proetzsch M., Berns K.: 3D Obstacle Detection and Avoidance in Vegetated Off-road Terrain. IEEE ICRA, pp. 923–928, Pasadena, USA, May 2008.

    Google Scholar 

  19. Vaskevicius N., Birk A., Pathak K. et al.: Fast Detection of Polygons in 3D Point Clouds from Noise-Prone Range Sensors. IEEE SSRR, 2007.

    Google Scholar 

  20. Xie B., Pan H., Xiang Z. et al.: Polarization-Based Water Hazards Detection for Autonomous Off-road Navigation. IEEE ICMA, pp. 1666–1670, 2007.

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Renner, A., Föhst, T., Berns, K. (2009). Perception of Environment Properties Relevant for Off-road Navigation. In: Dillmann, R., Beyerer, J., Stiller, C., Zöllner, J.M., Gindele, T. (eds) Autonome Mobile Systeme 2009. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10284-4_26

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