Neural implicit representations are drawing a lot of attention from the robotics community recently, as they are expressive, continuous and compact. However, city-scale continual implicit dense mapping based on sparse LiDAR input is still an under-explored challenge. To this end, we successfully build a city-scale continual neural mapping system with a panoptic representation that consists of environment-level and instance-level modelling. Given a stream of sparse LiDAR point cloud, it maintains a dynamic generative model that maps 3D coordinates to signed distance field (SDF) values. To address the difficulty of representing geometric information at different levels in city-scale space, we propose a tailored three-layer sampling strategy to dynamically sample the global, local and near-surface domains. Meanwhile, to realize high fidelity mapping of instance under incomplete observation, category-specific prior is introduced to better model the geometric details. We evaluate on the public SemanticKITTI dataset and demonstrate the significance of the newly proposed three-layer sampling strategy and panoptic representation, using both quantitative and qualitative results. Codes and model will be publicly available.

翻译：神经隐式表示因其表现力强、连续且紧凑的特性，近期在机器人领域引起了广泛关注。然而，基于稀疏LiDAR输入的城市尺度连续隐式稠密建图仍是一个尚未充分探索的挑战。为此，我们成功构建了一个城市尺度连续神经建图系统，采用由环境级和实例级建模组成的全景表示。给定稀疏LiDAR点云流，该系统维护一个动态生成模型，将三维坐标映射为符号距离场（SDF）值。为解决城市尺度空间中不同层级几何信息表达的困难，我们提出了一种定制化的三层采样策略，用于动态采样全局、局部和近表面域。同时，为实现不完整观测下实例的高保真建图，引入了类别先验以更好地建模几何细节。我们在公开的SemanticKITTI数据集上进行评估，并通过定量和定性结果展示了新提出的三层采样策略与全景表示的重要性。代码和模型将公开发布。