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.
@article{arxiv.2306.03641,
title = {Single-Shot Global Localization via Graph-Theoretic Correspondence Matching},
author = {Shigemichi Matsuzaki and Kenji Koide and Shuji Oishi and Masashi Yokozuka and Atsuhiko Banno},
journal= {arXiv preprint arXiv:2306.03641},
year = {2023}
}