FOSCo-Net: Ego-centric Voxel Completion for Forest LiDAR Occlusions

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.

Robotics (cs.RO)

arXiv:2611.06588 [cs.RO]

(or arXiv:2611.06588v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.06588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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