{"ID":2888420,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23629","arxiv_id":"2507.23629","title":"DRACo-SLAM2: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar EquippedUnderwater Robot Teams with Object Graph Matching","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.23629","url_pdf":"https://arxiv.org/pdf/2507.23629v1","authors":"[\"Yewei Huang\",\"John McConnell\",\"Xi Lin\",\"Brendan Englot\"]","published":"2025-07-31T15:08:50Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}