Joseph Modayil, Patrick Beeson and Benjamin Kuipers. 2004.
Using the topological skeleton for
scalable global metrical map-building.
IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS-04),
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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