Single-Shot Global Localization via Graph-Theoretic Correspondence Matching
This addresses localization for robotics/autonomous systems, offering a modality-agnostic method that is incremental over existing single-modality approaches.
The paper tackles global localization by matching instances between a query and prior map using a graph-theoretic maximum clique problem approach, achieving promising results on large-scale simulated urban scenes.
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.