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 topo-logical map of the spatial relationships between its obstacle-free areas. The eeciency 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 is used to interpret sensor readings. We present experimental results obtained with two diier-ent mobile robots, namely a Nomad 200 and a 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 partitionings consisting of simple rectangular partitions and reduces the complexity of treating the underlying geometrical properties .