City-scale Incremental Neural Mapping with Three-layer Sampling and Panoptic Representation

• URL: http://arxiv.org/abs/2209.14072v2
• Date: Wed, 12 Apr 2023 12:06:09 GMT
• Title: City-scale Incremental Neural Mapping with Three-layer Sampling and Panoptic Representation
• Authors: Yongliang Shi, Runyi Yang, Pengfei Li, Zirui Wu, Hao Zhao, Guyue Zhou
• Abstract summary: We 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 realize high fidelity mapping of instance under incomplete observation, category-specific prior is introduced to better model the geometric details.
• Score: 5.682979644056021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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