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

TL;DR

DRACo-SLAM2 introduces a distributed underwater SLAM framework that uses object graph matching and an improved measurement set maximization technique to enhance loop closure detection and map consistency for robot teams with imaging sonar.

Contribution

It presents a novel object graph representation and matching method for sonar maps, along with an incremental GCM approach tailored for underwater scan matching scenarios.

Findings

Effective inter-robot loop closure detection without prior geometric info

Improved robustness in scenarios with similar registration errors

Validated performance on simulated and real-world datasets

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.