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.

翻译：在无先验地图、全球定位系统受限且带宽有限的未知环境中，实现多无人地面车辆的鲁棒高效协同探索仍具挑战性：定位漂移会破坏地图一致性并导致覆盖冗余。本文提出一种全分布式探索框架，将描述符辅助的无人地面车辆间回环闭合与回环感知分层规划相耦合，同时支持自主定位与探索。我们开发了基于距离图像预对齐的轻量级激光雷达全局描述符，可在大幅偏航与横向偏移条件下实现鲁棒的跨无人地面车辆位置识别，并利用已验证的回环闭合维护全局一致轨迹与稀疏拓扑表征。进一步引入不确定性感知的跨无人地面车辆回环闭合筛选模块，该模块在姿态不确定性条件下对候选回环闭合进行评分，保留高利用率回环闭合作为全局任务分配与局部路径优化的规划锚点。仿真与真实无人地面车辆实验表明：回环闭合模块达到89.9%/95.5%的AR@1/AR@1%指标；分布式优化显著降低绝对轨迹误差；系统大幅减少双向通信流量；整体框架相比mTSP基线分别降低15%的探索时间与14%的行进距离。