Abstract
Terrain classification is a fundamental task in outdoor robot navigation to detect and avoid impassable terrain. Camera-based approaches are well-studied and provide good results. A drawback of these approaches, however, is that the quality of the classification varies with the prevailing lighting conditions. 3D laser scanners, on the other hand, are largely illumination-invariant. In this work we present easy to compute features for 3D point clouds using range and intensity values. We compare the classification results obtained using only the laser-based features with the results of camera-based classification and study the influence of different lighting conditions.
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© 2012 Springer-Verlag Berlin Heidelberg
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Laible, S., Khan, Y.N., Bohlmann, K., Zell, A. (2012). 3D LIDAR- and Camera-Based Terrain Classification Under Different Lighting Conditions. In: Levi, P., Zweigle, O., Häußermann, K., Eckstein, B. (eds) Autonomous Mobile Systems 2012. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32217-4_3
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DOI: https://doi.org/10.1007/978-3-642-32217-4_3
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