Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917445905678336 |
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| author | Cano, Lorenzo Mosteo, Alejandro R. Tardioli, Danilo |
| author_facet | Cano, Lorenzo Mosteo, Alejandro R. Tardioli, Danilo |
| contents | Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination may be uneven or nonexistent, and wall features are likely to be scarce, is a complex task, especially if the navigation has to be done at high speed. In this paper we propose a novel proof-of-concept navigation technique for UAVs based on the use of LiDAR information through the joint use of geometric and machine-learning algorithms. The perceived information is processed by a deep neural network to establish the yaw of the UAV with respect to the tunnel's longitudinal axis, in order to adjust the direction of navigation. Additionally, a geometric method is used to compute the safest location inside the tunnel (i.e. the one that maximizes the distance to the closest obstacle). This information proves to be sufficient for simple yet effective navigation in straight and curved tunnels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09688 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs Cano, Lorenzo Mosteo, Alejandro R. Tardioli, Danilo Robotics Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination may be uneven or nonexistent, and wall features are likely to be scarce, is a complex task, especially if the navigation has to be done at high speed. In this paper we propose a novel proof-of-concept navigation technique for UAVs based on the use of LiDAR information through the joint use of geometric and machine-learning algorithms. The perceived information is processed by a deep neural network to establish the yaw of the UAV with respect to the tunnel's longitudinal axis, in order to adjust the direction of navigation. Additionally, a geometric method is used to compute the safest location inside the tunnel (i.e. the one that maximizes the distance to the closest obstacle). This information proves to be sufficient for simple yet effective navigation in straight and curved tunnels. |
| title | Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.09688 |