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Abstract-Autonomous place detection has long been a major hurdle to topological map-building techniques. Theoretical work on topological mapping has assumed that places can be reliably detected by a robot, resulting in deterministic actions. Whether or not deterministic place detection is always achievable is controversial; however, even topological mapping algorithms that assume non-determinism benefit from highly reliable place detection. Unfortunately, topological map-building implementations often have handcoded place detection algorithms that are brittle and domain dependent.This paper presents an algorithm for reliable autonomous place detection that is sensor and domain independent. A preliminary implementation of this algorithm for an indoor robot has demonstrated reliable place detection in real-world environments, with no a priori environmental knowledge. The implementation uses a local, scrolling 2D occupancy grid and a real-time calculated Voronoi graph to find the skeleton of the free space in the local surround. In order to utilize the place detection algorithm in non-corridor environments, we also introduce the extended Voronoi graph (EVG), which seamlessly transitions from a skeleton of a midline in corridors to a skeleton that follows walls in rooms larger than the local scrolling map.

We propose a factored approach to mobile robot map-building that handles qualitatively different types of uncertainty by combining the strengths of topological and metrical approaches. Our framework is based on a computational model of the human cognitive map; thus it allows robust navigation and communication within several different spatial ontologies. This paper focuses exclusively on the issue of map-building using the framework. Our approach factors the mapping problem into natural sub-goals: building a metrical representation for local small-scale spaces; finding a topological map that represents the qualitative structure of large-scale space; and (when necessary) constructing a metrical representation for large-scale space using the skeleton provided by the topological map. We describe how to abstract a symbolic description of the robot’s immediate surround from local metrical models, how to combine these local symbolic models in order to build global symbolic models, and how to create a globally consistent metrical map from a topological skeleton by connecting local frames of reference.

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