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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Online-Zugang: | https://arxiv.org/abs/2311.02667 |
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| _version_ | 1866914293698527232 |
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| author | Bosello, Michael Aguiari, Davide Keuter, Yvo Pallotta, Enrico Kiade, Sara Caminati, Gyordan Pinzarrone, Flavio Halepota, Junaid Panerati, Jacopo Pau, Giovanni |
| author_facet | Bosello, Michael Aguiari, Davide Keuter, Yvo Pallotta, Enrico Kiade, Sara Caminati, Gyordan Pinzarrone, Flavio Halepota, Junaid Panerati, Jacopo Pau, Giovanni |
| contents | Unmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and evaluate these capabilities. Its challenges include accurate and robust visual-inertial odometry during aggressive maneuvers, complex aerodynamics, and constrained computational resources. As researchers increasingly channel their efforts into it, they also need the tools to timely and equitably compare their results and advances. With this dataset, we want to (i) support the development of new methods and (ii) establish quantitative comparisons for approaches originating from the broader robotics and artificial intelligence communities. We want to provide a one-stop resource that is comprehensive of (i) aggressive autonomous and piloted flight, (ii) high-resolution, high-frequency visual, inertial, and motion capture data, (iii) commands and control inputs, (iv) multiple light settings, and (v) corner-level labeling of drone racing gates. We also release the complete specifications to recreate our flight platform, using commercial off-the-shelf components and the open-source flight controller Betaflight, to democratize drone racing research. Our dataset, open-source scripts, and drone design are available at: https://github.com/tii-racing/drone-racing-dataset |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_02667 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Race Against the Machine: a Fully-annotated, Open-design Dataset of Autonomous and Piloted High-speed Flight Bosello, Michael Aguiari, Davide Keuter, Yvo Pallotta, Enrico Kiade, Sara Caminati, Gyordan Pinzarrone, Flavio Halepota, Junaid Panerati, Jacopo Pau, Giovanni Robotics Unmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and evaluate these capabilities. Its challenges include accurate and robust visual-inertial odometry during aggressive maneuvers, complex aerodynamics, and constrained computational resources. As researchers increasingly channel their efforts into it, they also need the tools to timely and equitably compare their results and advances. With this dataset, we want to (i) support the development of new methods and (ii) establish quantitative comparisons for approaches originating from the broader robotics and artificial intelligence communities. We want to provide a one-stop resource that is comprehensive of (i) aggressive autonomous and piloted flight, (ii) high-resolution, high-frequency visual, inertial, and motion capture data, (iii) commands and control inputs, (iv) multiple light settings, and (v) corner-level labeling of drone racing gates. We also release the complete specifications to recreate our flight platform, using commercial off-the-shelf components and the open-source flight controller Betaflight, to democratize drone racing research. Our dataset, open-source scripts, and drone design are available at: https://github.com/tii-racing/drone-racing-dataset |
| title | Race Against the Machine: a Fully-annotated, Open-design Dataset of Autonomous and Piloted High-speed Flight |
| topic | Robotics |
| url | https://arxiv.org/abs/2311.02667 |