This survey comprehensively reviews the evolving field of multi-robot collaborative Simultaneous Localization and Mapping (SLAM) using 3D Gaussian Splatting (3DGS). As an explicit scene representation, 3DGS has enabled unprecedented real-time, high-fidelity render- ing, ideal for robotics. However, its use in multi-robot systems introduces significant challenges in maintaining global consistency, managing communication, and fusing data from heterogeneous sources. We systematically categorize approaches by their architecture-centralized, distributed- and analyze core components like multi-agent consistency and alignment, communication- efficient, Gaussian representation, semantic distillation, fusion and pose optimization, and real- time scalability. In addition, a summary of critical datasets and evaluation metrics is provided to contextualize performance. Finally, we identify key open challenges and chart future research directions, including lifelong mapping, semantic association and mapping, multi-model for robustness, and bridging the Sim2Real gap.

翻译：本文全面综述了利用3D高斯泼溅（3DGS）进行多机器人协同即时定位与建图（SLAM）这一快速发展领域的研究进展。作为一种显式场景表示方法，3DGS实现了前所未有的实时高保真渲染能力，非常适用于机器人技术。然而，其在多机器人系统中的应用带来了维持全局一致性、管理通信以及融合异构数据源等方面的重大挑战。我们按系统架构（集中式、分布式）对现有方法进行了系统分类，并深入分析了多智能体一致性与对齐、通信效率、高斯表示、语义蒸馏、融合与位姿优化以及实时可扩展性等核心组件。此外，本文总结了关键数据集与评估指标，以量化性能表现。最后，我们指出了当前面临的主要开放挑战，并规划了未来研究方向，包括终身建图、语义关联与建图、提升鲁棒性的多模态方法以及弥合仿真与现实差距等。