3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM

2025-11-01

IEEE Robotics and Automation Letters

AI Summary

This work addresses the challenge of identifying map overlaps in decentralized multi-robot collaborative SLAM, where large viewpoint disparities hinder reliable loop closure detection. To overcome this, the study introduces 3D foundation models into the system for the first time, leveraging monocular image pairs to estimate inter-robot relative poses and enable cross-viewpoint loop closure. The authors propose a novel outlier suppression mechanism tailored to relative pose estimation and a scale-consistent pose graph optimization method, effectively mitigating the adverse effects of outliers and scale ambiguity. Experimental results demonstrate that the proposed approach outperforms existing methods in both localization and mapping accuracy while significantly improving computational and memory efficiency, making it well-suited for large-scale multi-robot scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) techniques often struggle to identify map overlaps due to significant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust loop closing approach that leverages these models to establish inter-robot measurements. In contrast to resource-intensive methods requiring full 3D reconstruction within a centralized map, our approach integrates foundation models into existing SLAM pipelines, yielding scalable and robust multi-robot mapping. Our contributions include: 1) integrating 3D foundation models to reliably estimate relative poses from monocular image pairs within decentralized C-SLAM; 2) introducing robust outlier mitigation techniques critical to the use of these relative poses and 3) developing specialized pose graph optimization formulations that efficiently resolve scale ambiguities. We evaluate our method against state-of-the-art approaches, demonstrating improvements in localization and mapping accuracy, alongside significant gains in computational and memory efficiency. These results highlight the potential of our approach for deployment in large-scale multi-robot scenarios.

Problem

• Decentralized Collaborative SLAM
• loop closing
• viewpoint variation
• map overlap
• multi-robot mapping

Innovation

• 3D foundation models
• decentralized collaborative SLAM
• loop closing
• relative pose estimation
• pose graph optimization