UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911953003216896 |
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| author | Triest, Samuel Fan, David D. Scherer, Sebastian Agha-Mohammadi, Ali-Akbar |
| author_facet | Triest, Samuel Fan, David D. Scherer, Sebastian Agha-Mohammadi, Ali-Akbar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08720 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments Triest, Samuel Fan, David D. Scherer, Sebastian Agha-Mohammadi, Ali-Akbar Robotics 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. |
| title | UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.08720 |