REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation

2025-03-05

AI Summary

To address key bottlenecks in large-scale LiDAR-based navigation—namely, high computational cost, sensitivity to viewpoint changes, and poor robustness in long-range loop closure detection—this paper proposes a subgraph consistency–based relocalization framework. Methodologically, it introduces geometric consistency verification, rather than embedding distance, as the primary loop closure criterion—a novel departure from conventional approaches. The framework jointly exploits rotation-invariant object features, object-level semantics, and graph neural networks (GNNs) to model local neighborhood structure, while employing a bag-of-words (BoW)–style global subgraph feature matching for efficient cross-view retrieval. Experimental results demonstrate that the method achieves state-of-the-art (SOTA) accuracy while doubling inference speed, significantly improving both efficiency and robustness for long-range and multi-view loop closure detection in large-scale environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point clouds for accurate place recognition do not scale well due to computationally expensive scan-to-scan comparisons. Alternative object-centric approaches are more efficient but often struggle with sensitivity to viewpoint variation. In this work, we introduce REGRACE, a novel approach that addresses these challenges of scalability and perspective difference in re-localization by using LiDAR-based submaps. We introduce rotation-invariant features for each labeled object and enhance them with neighborhood context through a graph neural network. To identify potential revisits, we employ a scalable bag-of-words approach, pooling one learned global feature per submap. Additionally, we define a revisit with geometrical consistency cues rather than embedding distance, allowing us to recognize far-away loop closures. Our evaluations demonstrate that REGRACE achieves similar results compared to state-of-the-art place recognition and registration baselines while being twice as fast.

Problem

• Addresses scalability issues in large-scale navigation re-localization.
• Improves robustness to viewpoint variations in object-centric approaches.
• Enhances efficiency and accuracy in loop closure detection.

Innovation

• Uses LiDAR-based submaps for scalable re-localization
• Employs rotation-invariant features with graph neural networks
• Detects loop closures using geometrical consistency cues