Personal tools
You are here: Home Publications Regional topological segmentation based on Mutual Information Graphs
About NIFTi
NIFTi is about human-robot cooperation. About teams of robots and humans doing tasks together, interacting together to try and reach a shared goal. NIFTi looks at how the robot could bear the human in mind. Literally. When determining what to do or say next in human-robot interaction; when, and how. NIFTi puts the human factor into cognitive robots, and human-robot team interaction in particular.   Each year, NIFTi evaluates its systems together with several USAR organizations. Rescue personnel teams up with NIFTi robots to carry out realistic missions, in real-life training areas. 
Impressum

This site uses Google Analytics to record statistics on site visits - see Legal information.

 

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.