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[摘要]：This paper presents a system for surface classification using a laser range finder. It is shown that the return intensities and range errors provide sufficient information to distinguish a wide range of surfaces commonly found in a number of environments. A supervised learning scheme (using curves representing the return intensity and range error as a function of angle of incidence) is used to classify the surface type of planar patches. Extensive experimental evidence is presented to demonstrate the potential of the proposed technique. The surface type classification, which uses a typical laser range finder, is targeted for use with autonomous robotic systems in which significantly different interaction is required for each of the various materials present. Results from an on-site experiment demonstrate that the information from the laser range finder is sufficient to identify the different materials (via their surface properties) present in a scene where a bridge structure is being prepared for grit blasting. |
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