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地图制作一直是机器人领域中一个长期存在的问题。最近神经非显式表示的进展为机器人地图制作带来了新的机会。在本文中,我们提出了一种名为NF-Atlas的多卷积神经特征场,该场通过姿态图优化与神经特征卷接桥接。我们将神经特征卷看作姿态图节点,将卷与卷之间的相对姿态看作姿态图边,使整个神经特征场在局部上变得刚性,并在全局上具有弹性。局部地,神经特征卷采用稀疏特征八叉树和小型MLP对子地图SDF进行编码,可选择语义。使用此结构学习地图允许端到端解决基于最大后验(MAP)的概率制图。全局上,地图是独立地一卷积接一卷积地构建的,避免了增量映射时的灾难性遗忘。此外,当发生回路闭合时,基于弹性姿态图的表示提供的功能只需要更新神经卷的原点而无需重新制图。最后,这些NF-Atlas的功能得到验证。由于稀疏性和优化式公式,NF-Atlas在仿真和实际数据集上都表现出了具有竞争力的精度、效率和内存使用率。