Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to map-building. We present a method for computing the global metrical map that builds on the structure provided by the topological map. This allows us to factor the uncertainty in the map into local metrical uncertainty (which is handled well by existing SLAM methods), global topological uncertainty (which is handled well by existing topological map-learning methods), and global metrical uncertainty (which can be handled effectively once the other types of uncertainty are factored out). We believe that this method for building the global metrical map will be scalable to very large environments.

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In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-04), 1530--1536, 2004.

@inproceedings{Modayil:IROS04b, title={Using the topological skeleton for scalable global metrical map-building}, author={Joseph Modayil and Patrick Beeson and Benjamin Kuipers}, booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-04)}, pages={1530--1536}, url="http://www.cs.utexas.edu/users/ai-lab/?Modayil:IROS04b", year={2004} }