Hierarchical localization using compact hybrid mapping for large-scale unstructured environments

Hierarchical localization frameworks provide efficient means for localizing a robot topologically and geometrically. The metric localization performance is enhanced since the searchable space is confined to a previously identified topological place. This adapts well to large-scale environments. In t...

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Bibliographic Details
Main Authors: Rady, S., Wagner, A., Badreddin, E.
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Feature extraction
hierarchical localization
hybrid map
Mathematical model
Measurement
qualitative features
Robot vision systems
triangulation
unstructured large-scale environments
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Description
Summary:Hierarchical localization frameworks provide efficient means for localizing a robot topologically and geometrically. The metric localization performance is enhanced since the searchable space is confined to a previously identified topological place. This adapts well to large-scale environments. In this paper, a two-level hierarchical localization using a compact hybrid map is presented. The map preserves information set at two different abstractions and resolutions, and possesses both geometric and non-geometric properties. The coarse-resolution information enables global topological localization through place matching. The higher-resolution enables local metric localization through triangulation. The presented hierarchical localization is totally independent on the robot's motion model. For effectiveness in both mapping and localization, the map is constructed based on information-theoretic evaluation that selects only highly qualitative information. The approach is demonstrated using a vision sensor and the scale invariant feature transform.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2011.6084033