Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917972315996160 |
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| author | Springer, Joshua Guðmundsson, Gylfi Þór Kyas, Marcel |
| author_facet | Springer, Joshua Guðmundsson, Gylfi Þór Kyas, Marcel |
| contents | A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15486 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments Springer, Joshua Guðmundsson, Gylfi Þór Kyas, Marcel Computer Vision and Pattern Recognition Machine Learning Robotics A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time. |
| title | Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2412.15486 |