  Simultaneously odometry and mapping using LiDAR data is an important task for
mobile systems to achieve full autonomy in large-scale environments. However,
most existing LiDAR-based methods prioritize tracking quality over
reconstruction quality. Although the recently developed neural radiance fields
(NeRF) have shown promising advances in implicit reconstruction for indoor
environments, the problem of simultaneous odometry and mapping for large-scale
scenarios using incremental LiDAR data remains unexplored. To bridge this gap,
in this paper, we propose a novel NeRF-based LiDAR odometry and mapping
approach, NeRF-LOAM, consisting of three modules neural odometry, neural
mapping, and mesh reconstruction. All these modules utilize our proposed neural
signed distance function, which separates LiDAR points into ground and
non-ground points to reduce Z-axis drift, optimizes odometry and voxel
embeddings concurrently, and in the end generates dense smooth mesh maps of the
environment. Moreover, this joint optimization allows our NeRF-LOAM to be
pre-trained free and exhibit strong generalization abilities when applied to
different environments. Extensive evaluations on three publicly available
datasets demonstrate that our approach achieves state-of-the-art odometry and
mapping performance, as well as a strong generalization in large-scale
environments utilizing LiDAR data. Furthermore, we perform multiple ablation
studies to validate the effectiveness of our network design. The implementation
of our approach will be made available at
https://github.com/JunyuanDeng/NeRF-LOAM.
