Multi-robot collaboration is becoming increasingly critical and presents significant challenges in modern robotics, especially for building a globally consistent, accurate map. Traditional multi-robot pose graph optimization (PGO) methods ensure basic global consistency but ignore the geometric structure of the map, and only use loop closures as constraints between pose nodes, leading to divergence and blurring in overlapping regions. To address this issue, we propose LEMON-Mapping, a loop-enhanced framework for large-scale, multi-session point cloud fusion and optimization. We re-examine the role of loops for multi-robot mapping and introduce three key innovations. First, we develop a robust loop processing mechanism that rejects outliers and a loop recall strategy to recover mistakenly removed but valid loops. Second, we introduce spatial bundle adjustment for multi-robot maps, reducing divergence and eliminating blurring in overlaps. Third, we design a PGO-based approach that leverages refined bundle adjustment constraints to propagate local accuracy to the entire map. We validate LEMON-Mapping on several public datasets and a self-collected dataset. The experimental results show superior mapping accuracy and global consistency of our framework compared to traditional merging methods. Scalability experiments also demonstrate its strong capability to handle scenarios involving numerous robots.

翻译：多机器人协作在现代机器人领域中日益关键且面临重大挑战，尤其是在构建全局一致且精准的地图方面。传统的多机器人位姿图优化方法虽能确保基本的全局一致性，却忽略了地图的几何结构，仅将闭环作为位姿节点间的约束，导致重叠区域出现发散与模糊。为解决这一问题，我们提出LEMON-Mapping，一种用于大规模多会话点云融合与优化的环增强框架。我们重新审视了环在多机器人建图中的作用，并引入三项关键创新。首先，我们开发了鲁棒的环处理机制以剔除异常值，并设计环召回策略以恢复被误删的有效环。其次，我们提出了面向多机器人地图的空间光束法平差，减少了重叠区域的发散并消除了模糊。第三，我们设计了一种基于位姿图优化的方法，利用精炼的光束法平差约束将局部精度传播至整个地图。我们在多个公开数据集及自采集数据集上验证了LEMON-Mapping。实验结果表明，与传统融合方法相比，我们的框架在建图精度和全局一致性方面表现更优。可扩展性实验也证实了其处理涉及大量机器人场景的强大能力。