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FOSCo-Net: Ego-centric Voxel Completion for Forest LiDAR Occlusions
arXiv cs.RO0 views
arXiv:2611.06588v1 Announce Type: new Abstract: Ground robots operating in temperate forests suffer from systematic LiDAR occlusions caused by tree trunks, creating persistent blind regions that degrade traversability estimation. In this paper, we propose FOSCo-Net, a self-supervised voxel completion framework that learns to reconstruct occluded geometry from a single scan using temporally accumulated observations as automatic supervision, and we evaluate cylindrical and Cartesian ego-centric grids with multiple decoder designs. FOSCo-Net consistently improves reconstruction quality and traversal safety, with a gated skip decoder achieving the most reliable performance and significantly reducing false obstacle predictions compared to baselines.
Source：https://arxiv.org/abs/2611.06588PreviousPlaying Soccer Like Humans Do: Learning Versatile and Agile Humanoid Soccer via Latent Motion PriorsNextDynamic Shape Control of Soft Trunk Robots
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