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 online. 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 obstacle-free 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 feedforward neural network is used to interpret sensor readings. We present experimental results obtained with two different mobile robots, the Nomad 200 and Khepera. The current implementation of the method relies on the assumption that obstacles are parallel or perpendicular to each other. This results in variable-resolution partitioning consisting of simple rectangular partitions and reduces the complexity of treating the underlying geometrical properties