A Distributed Multi-UGV Exploration Framework With Loop-Aware Planning and Descriptor-Aided Localization in Resource-Limited Environments

Robust and efficient cooperative exploration with multiple unmanned ground vehicles (UGVs) in unknown, GPS-denied, and bandwidth-limited environments without prior maps remains challenging, as localization drift degrades map consistency and induces redundant coverage. This article presents a fully distributed exploration framework that couples descriptor-aided inter-UGV loop closure with loop-aware hierarchical planning, while enabling autonomous localization and exploration. We develop a lightweight LiDAR global descriptor with range-image prealignment to enable robust cross-UGV place recognition under large yaw and lateral variations, and use verified loop closures to maintain globally consistent trajectories and a sparse topological representation. Importantly, we introduce an uncertainty-aware cross-UGV loop-closure selection module that scores candidate loop closures under pose uncertainty and retains high-utility loop closures as planning anchors, which guide hierarchical planning for global task allocation and local route refinement. Extensive simulations and real-UGV experiments demonstrate the effectiveness of the proposed system: the loop-closure module achieves AR@1/AR@1% of 89.9%/95.5%, distributed optimization reduces absolute trajectory error, the system substantially reduces two-way communication volume, and the overall framework reduces exploration time and travel distance by 15% and 14% compared with an mTSP baseline.