  This paper describes a method of global localization based on graph-theoretic
association of instances between a query and the prior map. The proposed
framework employs correspondence matching based on the maximum clique problem
(MCP). The framework is potentially applicable to other map and/or query
modalities thanks to the graph-based abstraction of the problem, while many of
existing global localization methods rely on a query and the dataset in the
same modality. We implement it with a semantically labeled 3D point cloud map,
and a semantic segmentation image as a query. Leveraging the graph-theoretic
framework, the proposed method realizes global localization exploiting only the
map and the query. The method shows promising results on multiple large-scale
simulated maps of urban scenes.
