Towards autonomous topological place detection using the Extended Voronoi Graph (2005)
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 hand-coded 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.
In IEEE International Conference on Robotics and Automation (ICRA-05) 2005.

Patrick Beeson Postdoctoral Alumni pbeeson [at] traclabs com
Nicholas Jong Ph.D. Alumni nickjong [at] me com
Benjamin Kuipers Formerly affiliated Faculty kuipers [at] cs utexas edu