  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/.
