Single-Shot Global Localization via Graph-Theoretic Correspondence Matching
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 6 Jun 2023.
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References · 34
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