综述多机器人协同定位与建图中3D高斯点云的应用与挑战
A Survey on Collaborative SLAM with 3D Gaussian Splatting
- 按中心化与分布式架构分类，分析多机一致性、通信效率等核心问题
- 指出当前方法在实时性与跨源数据融合上仍有瓶颈
- 适合研究多机器人系统、3D重建与协同感知的学者参考
本文全面综述了基于3D高斯点云（3DGS）的多机器人协同定位与建图（SLAM）的发展。作为显式场景表示，3DGS实现了前所未有的实时、高保真渲染，适用于机器人应用。然而，在多机器人系统中使用时，面临全局一致性维护、通信开销及异构数据融合等重大挑战。本文从架构角度系统分类方法，分析多智能体一致性对齐、通信效率、高斯表示、语义蒸馏、融合与位姿优化、实时可扩展性等核心组件。此外，总结了关键数据集与评估指标以衡量性能。最后，识别出若干开放性挑战，如持续建图、语义关联建图、多模型鲁棒性以及弥合仿真到现实的差距。
原文摘要 · Abstract (English)
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 rendering, 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.
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