Neural implicit representations have emerged as a promising solution for providing dense geometry in Simultaneous Localization and Mapping (SLAM). However, existing methods in this direction fall short in terms of global consistency and low latency. This paper presents NGEL-SLAM to tackle the above challenges. To ensure global consistency, our system leverages a traditional feature-based tracking module that incorporates loop closure. Additionally, we maintain a global consistent map by representing the scene using multiple neural implicit fields, enabling quick adjustment to the loop closure. Moreover, our system allows for fast convergence through the use of octree-based implicit representations. The combination of rapid response to loop closure and fast convergence makes our system a truly low-latency system that achieves global consistency. Our system enables rendering high-fidelity RGB-D images, along with extracting dense and complete surfaces. Experiments on both synthetic and real-world datasets suggest that our system achieves state-of-the-art tracking and mapping accuracy while maintaining low latency.

翻译：神经隐式表示已成为同时在定位与地图构建（SLAM）中提供稠密几何结构的极具前景的解决方案。然而，现有此类方法在全局一致性和低延迟方面存在不足。本文提出NGEL-SLAM以应对上述挑战。为确保全局一致性，我们的系统利用融合了回环检测的传统特征跟踪模块。此外，我们通过采用多个神经隐式场表示场景，使系统能够快速响应回环调整，从而维护全局一致的地图。同时，基于八叉树的隐式表示使系统能够快速收敛。对回环的快速响应与快速收敛的结合，使我们的系统成为真正实现全局一致性的低延迟系统。本系统可渲染高保真RGB-D图像，并提取稠密且完整的表面。在合成数据集和真实世界数据集上的实验表明，本系统在保持低延迟的同时，实现了最先进的跟踪与建图精度。