  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.
