Single-Shot Global Localization via Graph-Theoretic Correspondence Matching (2306.03641v1)

Abstract: 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.

• The paper introduces an innovative graph-theoretic framework that transforms global localization into a maximum clique problem for effective instance matching.
• It demonstrates the integration of diverse modalities, including 3D semantically labeled point clouds and segmentation images, to infer accurate robot poses.
• Experimental results in simulated urban environments validate the method's accuracy, providing a robust solution for GNSS-challenged navigation scenarios.

Single-Shot Global Localization: A Graph-Theoretic Approach

The paper presents an innovative method for single-shot global localization utilizing a graph-theoretic framework to address the problem of instance correspondence matching between a query and a prior map. The methodology is predicated on solving the maximum clique problem (MCP) within this framework, enabling the abstraction and application of various map and query modalities, which is a significant departure from conventional methods that often require compatible sensory modalities between the query and the map dataset.

Methodological Framework

The core of this approach lies in the transformation of the localization problem into a graph-theoretic setting, wherein the correspondences between query and map instances are represented as nodes in a graph. Edges between nodes signify consistency based on predefined criteria, allowing the utilization of MCP to identify the most consistent set of correspondences, which is then used to infer the robot's pose. The framework notably utilizes 3D semantically labeled point cloud maps and semantic segmentation images as modalities, showcasing its versatility.

Numerical Performance and Claims

The framework was tested in large-scale simulated urban environments to evaluate its effectiveness. The results across multiple simulated urban scenes demonstrated promising metrics, especially in terms of the framework's ability to provide accurate localization relying only on map and query data. The authors report the successful localization of mobile robots and autonomous vehicles even in challenging and open spaces where traditional methods reliant on GNSS might struggle.

Implications and Future Developments

The methodology offers significant implications for the field of robotics and autonomous navigation, as it provides a potential pathway for enhanced localization without the strict requirement of modality compatibility. This can lead to broader applications across different types of maps and sensor modalities, offering unprecedented flexibility and utility.

In terms of future developments, improving the robustness of the pose calculation mechanism, especially in noise-laden environments, presents a viable direction. Additionally, further exploration of the descriptor design could mitigate challenges such as insufficient mapping of distant objects, which constrains the current implementation.

The utilization of graph-theoretic approaches in solving correspondence matching presents a novel solution space and establishes a promising precedent for addressing cross-modal global localization challenges. Future research might explore extending this methodology to even more diverse datasets or integrating more complex consistency criteria to enhance performance in varied and real-world environments. Such advancements may solidify the role of graph-theoretic methods in the field of robotics localization and beyond.