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.

翻译：激光雷达建图是机器人领域长期存在的基础问题。近年来神经隐式表示的研究进展为机器人建图带来了新机遇。本文提出多体素神经特征场（NF-Atlas），该方法将神经特征体素与位姿图优化相结合。通过将神经特征体素视为位姿图节点，体素间相对位姿视为位姿图边，整个神经特征场实现局部刚性与全局弹性特性。在局部层面，神经特征体素采用稀疏特征八叉树与小型MLP，在可选语义信息辅助下编码子地图SDF。基于该结构的建图学习可端到端求解最大后验概率建图问题。在全局层面，地图以体素为单元独立构建，避免增量建图过程中的灾难性遗忘。当回环检测发生时，基于弹性位姿图表示仅需更新神经体素原点坐标而无需重新建图。最终验证了NF-Atlas的各项功能。得益于稀疏性与基于优化的设计范式，NF-Atlas在仿真与真实数据集上展现出具有竞争力的精度、效率与内存占用表现。