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Neural implicit representations have emerged as a promising solution forproviding dense geometry in Simultaneous Localization and Mapping (SLAM).However, existing methods in this direction fall short in terms of globalconsistency and low latency. This paper presents NGEL-SLAM to tackle the abovechallenges. To ensure global consistency, our system leverages a traditionalfeature-based tracking module that incorporates loop closure. Additionally, wemaintain a global consistent map by representing the scene using multipleneural implicit fields, enabling quick adjustment to the loop closure.Moreover, our system allows for fast convergence through the use ofoctree-based implicit representations. The combination of rapid response toloop closure and fast convergence makes our system a truly low-latency systemthat achieves global consistency. Our system enables rendering high-fidelityRGB-D images, along with extracting dense and complete surfaces. Experiments onboth synthetic and real-world datasets suggest that our system achievesstate-of-the-art tracking and mapping accuracy while maintaining low latency.

NGEL-SLAM: Neural Implicit Representation-based Global Consistent Low-Latency SLAM System