Gaussian Splatting has garnered widespread attention due to its exceptional performance. Consequently, SLAM systems based on Gaussian Splatting have emerged, leveraging its 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 adjustments and scene generalization capabilities. To address these issues, we introduce NGM-SLAM, the first GS-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 have developed neural implicit 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 gap filling and high-quality scene expression, supporting both monocular, stereo, and RGB-D inputs, and achieving state-of-the-art scene reconstruction and tracking performance.

翻译：高斯泼溅技术因其卓越性能而受到广泛关注。基于高斯泼溅的SLAM系统随之涌现，利用其快速实时渲染和高保真建图能力。然而，当前的高斯泼溅SLAM系统通常难以处理大规模场景表达，且缺乏有效的闭环调整和场景泛化能力。针对这些问题，我们提出NGM-SLAM——首个利用神经辐射场子图进行渐进式场景表达的GS-SLAM系统，有效融合了神经辐射场与三维高斯泼溅的优势。我们开发了神经隐式子图作为监督信号，并通过融合子图的高斯渲染实现高质量场景表达与在线闭环调整。在多个真实场景和大规模场景数据集上的实验结果表明，本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、立体及RGB-D输入，在场景重建与跟踪性能上均达到当前最优水平。