UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments
<|reference_start|>UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments: Traversability estimation in rugged, unstructured environments remains a challenging problem in field robotics. Often, the need for precise, accurate traversability estimation is in direct opposition to the limited sensing and compute capability present on affordable, small-scale mobile robots. To address this issue, we present a novel method to learn [u]ncertainty-aware [n]avigation features from high-fidelity scans of [real]-world environments (UNRealNet). This network can be deployed on-robot to predict these high-fidelity features using input from lower-quality sensors. UNRealNet predicts dense, metric-space features directly from single-frame lidar scans, thus reducing the effects of occlusion and odometry error. Our approach is label-free, and is able to produce traversability estimates that are robot-agnostic. Additionally, we can leverage UNRealNet's predictive uncertainty to both produce risk-aware traversability estimates, and refine our feature predictions over time. We find that our method outperforms traditional local mapping and inpainting baselines by up to 40%, and demonstrate its efficacy on multiple legged platforms.<|reference_end|>
@article{triest2024unrealnet:,
    title={UNRealNet: Learning Uncertainty-Aware Navigation Features from
  High-Fidelity Scans of Real Environments},
    author={Samuel Triest, David D. Fan, Sebastian Scherer, and Ali-Akbar
  Agha-Mohammadi},
    journal={arXiv preprint arXiv:2407.08720},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08720},
    primaryClass={cs.RO}
}