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M. Liu, F. Colas, and R. Siegwart (2011)

Regional topological segmentation based on Mutual Information Graphs

In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA 2011).

When people communicate with the robots, the most direct mean is by naming the different regions in the environment. This capability of robot is highly depending on the unsupervised topological segmentation results. Nowadays lots of researches in this direction are based on the spectral clustering algorithm in graph theory. However there are inherent drawbacks of spectral clustering algorithms. In this paper, we first discuss these drawbacks using several testing results, then we propose our approach based on information theory in the formation of the graph structure, and uses Chow-Liu tree to segment the composed graph according to the weights’ variations. The results show that our method provides a more flexible and rational result in the sense of aiding the semantic mapping or other further applications.