Visual terrain mapping for traversable path planning of mobile robots

Amir Shirkhodaie; Rachida Amrani; Edward W. Tunstel

doi:10.1117/12.579298

25 October 2004 Visual terrain mapping for traversable path planning of mobile robots

Amir Shirkhodaie, Rachida Amrani, Edward W. Tunstel

Proceedings Volume 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision; (2004) https://doi.org/10.1117/12.579298
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Citation Download Citation

Amir Shirkhodaie, Rachida Amrani, and Edward W. Tunstel "Visual terrain mapping for traversable path planning of mobile robots", Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); https://doi.org/10.1117/12.579298

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