Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning to explore and build maps
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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A map is an abstract internal representation of an environment for a mobile robot, and how to learn it autonomously is one of the most fundamental issues in the research fields of intelligent robotics and artificial intelligence. In this paper, we propose a topological map learning method for mobile robots which constructs a POMDP-based discrete state transition model from time-series data of observations and actions. The main point of this method is to find a set of states or nodes of the map gradually so that it minimizes the three types of entropies or uncertainties of the map about "what observations are obtained", "what actions are available" and "what state transitions are expected". It is shown that the topological structure of the state transition model is effectively obtained by this method.