  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.
