Topological and metrical methods for representing spatial knowledge have complementary strengths. We present a hybrid extension to the Spatial Semantic Hierarchy that combines their strengths and avoids their weaknesses. Metrical SLAM methods are used to build local maps of small-scale space within the sensory horizon of the agent, while topological methods are used to represent the structure of large-scale space. We describe how a local perceptual map is analyzed to identify a local topology description and is abstracted to a topological place. The map-building method creates a set of topological map hypotheses that are consistent with travel experience. The set of maps is guaranteed under reasonable assumptions to include the correct map. We demonstrate the method on a real environment with multiple nested large-scale loops.

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In IEEE International Conference on Robotics and Automation (ICRA-04), 2004.

@inproceedings{kuipers:icra04, title={Local metrical and global topological maps in the Hybrid Spatial Semantic Hierarchy}, author={Benjamin Kuipers and Joseph Modayil and Patrick Beeson and Matt MacMahon and Francesco Savelli}, booktitle={IEEE International Conference on Robotics and Automation (ICRA-04)}, url="http://www.cs.utexas.edu/users/ai-lab/?kuipers:icra04", year={2004} }