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A mobile robot that accomplishes high level tasks needs to be able to classify the objects in the environment and to determine their location. In this paper, the authors address the problem of online object detection in 3D laser range data. The object classes are represented by 3D point-clouds that can be obtained from a set of range scans. Their method relies on the extraction of point features from range images that are computed from the point-clouds. Compared to techniques that directly operate on a full 3D representation of the environment, their approach requires less computation time while retaining the robustness of full 3D matching.
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