SLAM systems based on Gaussian Splatting have garnered attention due to their capabilities for rapid real-time rendering and high-fidelity mapping. However, current Gaussian Splatting SLAM systems usually struggle with large scene representation and lack effective loop closure detection. To address these issues, we introduce NGM-SLAM, the first 3DGS based SLAM system that utilizes neural radiance field submaps for progressive scene expression, effectively integrating the strengths of neural radiance fields and 3D Gaussian Splatting. We utilize neural radiance field submaps as supervision and achieve high-quality scene expression and online loop closure adjustments through Gaussian rendering of fused submaps. Our results on multiple real-world scenes and large-scale scene datasets demonstrate that our method can achieve accurate hole filling and high-quality scene expression, supporting monocular, stereo, and RGB-D inputs, and achieving state-of-the-art scene reconstruction and tracking performance.

翻译：基于高斯溅射的SLAM系统因其快速实时渲染与高保真建图能力而备受关注。然而，当前的高斯溅射SLAM系统通常难以表征大规模场景，且缺乏有效的闭环检测机制。为解决这些问题，我们提出了NGM-SLAM——首个基于3D高斯溅射并利用神经辐射场子地图进行渐进式场景表达的SLAM系统，有效融合了神经辐射场与3D高斯溅射的优势。我们以神经辐射场子地图作为监督，通过对融合子地图的高斯渲染，实现了高质量的场景表达与在线闭环调整。在多个真实场景及大规模场景数据集上的实验结果表明，我们的方法能够实现精确的空洞填补与高质量场景表达，支持单目、双目及RGB-D输入，并在场景重建与跟踪性能上达到了领先水平。