We describe a novel method for classifying terrain in unstructured, natural environments for the purpose of aiding mobile robot navigation. This method operates on range data provided by stereo without the traditional preliminary extraction of geometric features such as height and slope, replacing these measurements with 2D histograms representing the shape and permeability of objects within a local region. A convolutional neural network is trained to categorize the histogram samples according to the traversability of the terrain they represent for a small mobile robot. In live and offline testing in a wide variety of environments, it demonstrates state-of-the-art performance.
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Happold, M., Ollis, M. (2007). Using Learned Features from 3D Data for Robot Navigation. In: Mukhopadhyay, S.C., Gupta, G.S. (eds) Autonomous Robots and Agents. Studies in Computational Intelligence, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73424-6_8
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DOI: https://doi.org/10.1007/978-3-540-73424-6_8
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