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arXiv · 2507.23629

DRACo-SLAM2: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar EquippedUnderwater Robot Teams with Object Graph Matching

Yewei Huang·John McConnell·Xi Lin·Brendan Englot
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Abstract

We present DRACo-SLAM2, a distributed SLAM framework for underwater robot teams equipped with multibeam imaging sonar. This framework improves upon the original DRACo-SLAM by introducing a novel representation of sonar maps as object graphs and utilizing object graph matching to achieve time-efficient inter-robot loop closure detection without relying on prior geometric information. To better-accommodate the needs and characteristics of underwater scan matching, we propose incremental Group-wise Consistent Measurement Set Maximization (GCM), a modification of Pairwise Consistent Measurement Set Maximization (PCM), which effectively handles scenarios where nearby inter-robot loop closures share similar registration errors. The proposed approach is validated through extensive comparative analyses on simulated and real-world datasets.

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Yewei Huang, John McConnell, Xi Lin, Brendan Englot. 2025-07-31. DRACo-SLAM2: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar EquippedUnderwater Robot Teams with Object Graph Matching. https://arxiv.org/abs/2507.23629

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