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

arXiv: https://arxiv.org/abs/2507.23629 · HTML: https://mlsanity.com/paper/2507.23629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle](https://mlsanity.com/paper/2605.01949/) (2026)

## Abstract (arXiv)

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.