Abstract
In this article we describe an algorithm for constructing a compact representation of 3D laser range data. Our approach extracts a dictionary of local scans from the scene. The words of this dictionary are used to replace recurrent local 3D structures, which leads to a substantial compression of the entire point cloud. We optimize our model in terms of complexity and accuracy by minimizing the Bayesian information criterion (BIC). Experimental evaluations on large real-world datasets show that the described method allows robots to accurately reconstruct environments with as few as 70 words. Furthermore the experiments suggest that the proposed representation gives a richer semantic description than pure occupancy based representations.
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Ruhnke, M., Steder, B., Grisetti, G., Burgard, W. (2012). 3D Environment Modeling Based on Surface Primitives. In: Prassler, E., et al. Towards Service Robots for Everyday Environments. Springer Tracts in Advanced Robotics, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25116-0_19
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DOI: https://doi.org/10.1007/978-3-642-25116-0_19
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