Efficient Learning of Variable-Resolution Cognitive Maps for Autonomous Indoor Navigation


. This paper presents an adaptive method that allows mobile robots to learn cognitive maps of indoor environments incrementally and on-line. Our approach models the environment by means of a variable-resolution partitioning that discretizes the world in perceptually homogeneous regions. The resulting model incorporates both a compact geometrical representation of the environment and a topological map of the spatial relationships between its obstaclesfree areas. The efficiency of the learning process is based on the use of local memory-based techniques for partitioning and of active learning techniques for selecting the most appropriate region to be explored next. In addition, a feed-forward neural network interprets sensor readings efficiently. Finally, we present experimental results obtained with two extremely different mobile robots, namely a Nomad 200 and a Khepera, which confirm the validity and generality of our approach. keywords: map learning, variable-resolution partitioning,..

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