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：LiDAR制图一直是机器人领域的长期难题。神经隐式表示的最新进展为机器人制图带来了新的机遇。本文提出一种名为NF-Atlas的多体素神经特征场，将神经特征体素与位姿图优化相连接。通过将神经特征体素作为位姿图节点、体素间相对位姿作为位姿图边，整个神经特征场同时具备局部刚性与全局弹性。局部层面，神经特征体素采用稀疏特征八叉树和小型MLP编码子地图SDF，并支持语义拓展。利用该结构学习地图可端到端求解基于最大后验概率（MAP）的概率制图问题。全局层面，地图以体素为单位独立构建，避免了增量制图中的灾难性遗忘。当闭环发生时，基于弹性位姿图表示仅需更新神经体素原点而无需重新建图。最后验证了NF-Atlas的上述功能。得益于稀疏性与基于优化的形式化方法，NF-Atlas在模拟数据集和真实世界数据集上均展现出具有竞争力的精度、效率与内存占用性能。