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

• URL: http://arxiv.org/abs/2606.11088v1
• Date: Tue, 09 Jun 2026 16:48:53 GMT
• Title: A Distributed Multi-UGV Exploration Framework With Loop-Aware Planning and Descriptor-Aided Localization in Resource-Limited Environments
• Abstract summary: This paper presents a fully distributed exploration framework that couples descriptoraided inter-UGV loop closure with loop-aware hierarchical planning.<n>We develop a lightweight LiDAR global descriptor with range-image prealignment to enable robust cross-UGV place recognition.<n>We also introduce an uncertainty-aware crossUGV loop-closure selection module that scores candidate loop closures under pose uncertainty and retains high-utility loop closures.
• Score: 13.456466206579782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust and efficient cooperative exploration with multiple unmanned ground vehicles (UGVs) in unknown, GPSdenied, and bandwidth-limited environments without prior maps remains challenging, as localization drift degrades map consistency and induces redundant coverage. This paper presents a fully distributed exploration framework that couples descriptoraided 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. We further introduce an uncertainty-aware crossUGV loop-closure selection module that scores candidate loop closures under pose uncertainty and retains high-utility loop closures as planning anchors for global task allocation and local route refinement. Simulations and real-UGV experiments show that 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%, respectively, compared with an mTSP baseline.

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