Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation. This prevents efficient incorporation of loop closure constraints and limits scalability. To address these shortcomings, we propose a neural mapping framework which anchors lightweight neural fields to the pose graph of a sparse visual SLAM system. Our approach shows the ability to integrate large-scale loop closures, while limiting necessary reintegration. Furthermore, we verify the scalability of our approach by demonstrating successful building-scale mapping taking multiple loop closures into account during the optimization, and show that our method outperforms existing state-of-the-art approaches on large scenes in terms of quality and runtime. Our code is available at https://kth-rpl.github.io/neural_graph_mapping/.

翻译：现有的基于神经场的SLAM方法通常采用单一整体场作为场景表示，这阻碍了回环约束的高效集成并限制了可扩展性。为解决这些缺陷，我们提出了一种神经映射框架，将轻量级的神经场锚定到稀疏视觉SLAM系统的位姿图上。我们的方法展示了整合大规模回环闭合的能力，同时限制了必要的重新整合。此外，我们通过展示在优化过程中考虑多个回环闭合的成功建筑级映射，验证了该方法的可扩展性，并证明该方法在大型场景中的质量和运行时间均优于现有最先进方法。我们的代码可在 https://kth-rpl.github.io/neural_graph_mapping/ 获取。