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

Prashant Kumar · Kshitij Madhav Bhat · Vedang Bhupesh Shenvi Nadkarni · Prem Kalra

Arch 4A-E Poster #53
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Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 32× 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 generate a valuable byproduct - an accurate binary segmentation mask of static and dynamic objects that is helpful for navigation planning and safety in constrained environment.

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