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Feature extraction is a central step of processing LIght Detection And Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. The authors describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Their method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector.
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