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GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds

2023-11-29 20:59:00
Prashant Kumar, Kshitij Madhav Bhat, Vedang Bhupesh Shenvi Nadkarni, Prem Kalra


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.

Abstract (translated)

稀疏的激光雷达点云导致静态结构细节丧失严重,并降低了静态点密度,从而降低了可用于导航的静态点的密度。减少密度在几种场景下对导航都可能造成严重后果。我们观察到,尽管点密度较高,但在大多数情况下,激光雷达轮廓的静态结构全局拓扑结构可以推断出来。利用这一特性,我们获得了以单连接组件的形式表示静态激光雷达扫描骨架,作为其全局拓扑结构的代理。利用骨架,我们通过填充静态结构中的新点来克服稀疏性。新引入的点可能对应于现有的静态结构或被动态物体 earlier 遮挡的静态点。据我们所知,这是第一个使用这种策略处理稀疏激光雷达点云的。与我们的方法接近的现有解决方案未能识别并保留全局静态激光雷达拓扑结构,并生成次优点。我们提出了GLiDR,一种基于0维 persistent homology(PH)约束的图生成网络。这使得GLiDR能够沿全局静态激光雷达骨架引入新的静态点。GLiDR通过使用32x稀疏动态扫描生成精确的静态点,在三个数据集上的表现优于基线。新引入的静态点使GLiDR在几种场景中能够超过基于激光雷达的导航。GLiDR生成有价值的产品 - 准确的二进制分割掩码静态和动态物体,这对于导航规划和在约束环境中的安全驾驶非常有益。



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