AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Unsupervised Feature Learning for 3D Scene Reconstruction with Occupancy Maps
Vitor Campanholo Guizilini, Fabio Tozeto Ramos

Last modified: 2017-02-12


This paper addresses the task of unsupervised feature learning for three-dimensional occupancy mapping, as a way to segment higher-level structures based on raw unorganized point cloud data. In particular, we focus on detecting planar surfaces, which are common in most structured or semi-structured environments. This segmentation is then used to minimize the amount of parameters necessary to properly create a 3D occupancy model of the surveyed space, thus increasing computational speed and decreasing memory requirements. As the 3D modeling tool, an extension to Hilbert Maps was selected, since it naturally uses a feature-based representation of the environment to achieve real-time performance. Experiments conducted in simulated and real large-scale datasets show a substantial gain in performance, while decreasing the amount of stored information by orders of magnitude without sacrificing accuracy.


Mapping; Kernel Methods; Scene Reconstruction; 3D models; Feature Learning

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