Sparse LiDAR point clouds cause severe loss of detail of static structures and reduce the density of static points available for navigation. Reduced density can be detrimental to navigation under several scenarios. We observe that despite high sparsity, in most cases, the global topology of LiDAR outlining the static structures can be inferred. We utilize this property to obtain a backbone skeleton of a static LiDAR scan in the form of a single connected component that is a proxy to its global topology. We utilize the backbone to augment new points along static structures to overcome sparsity. Newly introduced points could correspond to existing static structures or to static points that were earlier obstructed by dynamic objects. To the best of our knowledge, we are the first to use this strategy for sparse LiDAR point clouds. Existing solutions close to our approach fail to identify and preserve the global static LiDAR topology and generate sub-optimal points. We propose GLiDR, a Graph Generative network that is topologically regularized using 0-dimensional Persistent Homology (PH) constraints. This enables GLiDR to introduce newer static points along a topologically consistent global static LiDAR backbone. GLiDR generates precise static points using 32x sparser dynamic scans and performs better than the baselines across three datasets. The newly introduced static points allow GLiDR to outperform LiDAR-based navigation using SLAM in several settings. GLiDR generates a valuable byproduct - an accurate binary segmentation mask of static and dynamic objects that is helpful for navigation planning and safety in constrained environments.

稀疏的LiDAR点云常常导致静态结构的细节丢失严重，并减少了用于导航的静态点的密度。本文提出了一种利用全局拓扑信息推断静态结构的方法，通过构建骨架并在静态结构上生成新的点，以克服稀疏性问题。我们提出了GLiDR，这是一个通过0维持久同调约束实现拓扑规范化的图生成网络。GLiDR使用比基准方法更少的动态扫描点生成更精确的静态点，并在三个数据集上表现出更好的性能。GLiDR还能生成准确的静态和动态对象的二值分割掩模，有助于导航规划和有限环境中的安全性。

GLiDR: 基于拓扑约束的稀疏激光雷达点云生成网络