Efficient multi-agent 3D mapping is essential for robotic teams operating in unknown environments, but dense representations hinder real-time exchange over constrained communication links. In multi-agent Simultaneous Localization and Mapping (SLAM), systems typically rely on a centralized server to merge and optimize the local maps produced by individual agents. However, sharing these large map representations, particularly those generated by recent methods such as Gaussian Splatting, becomes a bottleneck in real-world scenarios with limited bandwidth. We present an improved multi-agent RGB-D Gaussian Splatting SLAM framework that reduces communication load while preserving map fidelity. First, we incorporate a compaction step into our SLAM system to remove redundant 3D Gaussians, without degrading the rendering quality. Second, our approach performs centralized loop closure computation without initial guess, operating in two modes: a pure rendered-depth mode that requires no data beyond the 3D Gaussians, and a camera-depth mode that includes lightweight depth images for improved registration accuracy and additional Gaussian pruning. Evaluation on both synthetic and real-world datasets shows up to 85-95\% reduction in transmitted data compared to state-of-the-art approaches in both modes, bringing 3D Gaussian multi-agent SLAM closer to practical deployment in real-world scenarios. Code: https://github.com/lemonci/coko-slam

翻译：多智能体三维建图对于在未知环境中运行的机器人团队至关重要，但密集表示形式会阻碍通过受限通信链路进行实时交换。在多智能体同步定位与建图（SLAM）系统中，系统通常依赖集中式服务器合并和优化各智能体生成的局部地图。然而，共享这些大规模地图表示（尤其是高斯溅射等最新方法生成的表示）在带宽有限的实际场景中构成了瓶颈。我们提出了一种改进的多智能体RGB-D高斯溅射SLAM框架，该框架能够在保持地图保真度的同时降低通信负载。首先，我们在SLAM系统中引入压缩步骤，以去除冗余的三维高斯体而不降低渲染质量。其次，我们的方法无需初始猜测即可执行集中式回环检测计算，支持两种模式：纯渲染深度模式仅需三维高斯体数据，而相机深度模式则包含轻量级深度图像以提升配准精度并实现额外的高斯体剪枝。在合成数据集和真实数据集上的评估表明，与现有最优方法相比，两种模式下的传输数据量减少了85-95%，使三维高斯多智能体SLAM更接近实际场景中的部署应用。代码：https://github.com/lemonci/coko-slam