High-level map representation providing object-based understanding of the environment is an important component for SLAM. We present a novel algorithm to build plane object-based map representation upon point cloud that is obtained in real-time from RGB-D sensors such as Kinect. On the basis of segmented planes in point cloud we construct a graph, where a node and edge represent a plane and its real intersection with other plane, respectively. After that, we extract all trihedral angles (corners) represented by 3rd order cycles in the graph. Afterwards, we execute systematic aggregation of trihedral angles into object such as trihedral angles of the same plane-based object have common edges. Finally, we classify objects using simple subgraph patterns and determine their physical sizes. Our experiments figured out that the proposed algorithm reliably extracts objects, determines their physical sizes and classifies them with a promising performance.
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