Patrick Beeson, Matt MacMahon, Joseph Modayil, Jefferson Provost,
Francesco Savelli and Benjamin Kuipers. 2003.
Exploiting local perceptual models for topological
IJCAI-2003 Workshop on Reasoning with Uncertainty in Robotics (RUR-03).
The Spatial Semantic Hierarchy (SSH) provides a robot-independent
ontology and logical theory for building topological maps of
large-scale environments online. Existing SSH implementations make
very limited use of perceptual information and thus create many
candidate maps. Metrical mapping implementations capture detailed
knowledge about local small-scale space but do not handle large
environments well due to computational limitations and global metrical
uncertainty. In this paper, we extend the SSH to utilize better
sensory information by incorporating information derived from local
metrical models into the large-scale space framework. This new
extension of the Spatial Semantic Hierarchy uses local topology
obtained from local perceptual models to constrain a global
topological map search.
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