Large-scale feature identification for indoor topological mapping

The paper describes a new approach to creating a map of the environment for a mobile robot by identifying large-scale indoor features directly from sonar observations. Current research in creating environmental maps for mobile robots either use grid-based representations, or use small-scale features to construct larger entities for topological maps, or use combinations of these approaches. These methods do not directly identify features on the scale of corridors, alcoves, and intersections that have high semantic content for people and provide a compact representation of the topology of the environment. We present a simple rule-based technique to differentiate and identify a set of large-scale indoor features. The rule-based approach is used to demonstrate the potential of our method. The experimental results show that processor intensive techniques such as pattern recognition/signal processing, neural networks, or clustering algorithms may not be required for successful, direct large-scale feature identification.