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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 a 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 recently developed 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 is scalable to very large environments.