  LiDAR Mapping has been a long-standing problem in robotics. Recent progress
in neural implicit representation has brought new opportunities to robotic
mapping. In this paper, we propose the multi-volume neural feature fields,
called NF-Atlas, which bridge the neural feature volumes with pose graph
optimization. By regarding the neural feature volume as pose graph nodes and
the relative pose between volumes as pose graph edges, the entire neural
feature field becomes both locally rigid and globally elastic. Locally, the
neural feature volume employs a sparse feature Octree and a small MLP to encode
the submap SDF with an option of semantics. Learning the map using this
structure allows for end-to-end solving of maximum a posteriori (MAP) based
probabilistic mapping. Globally, the map is built volume by volume
independently, avoiding catastrophic forgetting when mapping incrementally.
Furthermore, when a loop closure occurs, with the elastic pose graph based
representation, only updating the origin of neural volumes is required without
remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to
the sparsity and the optimization based formulation, NF-Atlas shows competitive
performance in terms of accuracy, efficiency and memory usage on both
simulation and real-world datasets.
