Academic Journal
PencilNet:Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing
| العنوان: | PencilNet:Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing |
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| المؤلفون: | Pham, Xuan Huy, Sarabakha, Andriy, Odnoshyvkin, Mykola, Kayacan, Erdal |
| المصدر: | Pham , X H , Sarabakha , A , Odnoshyvkin , M & Kayacan , E 2022 , ' PencilNet : Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing ' , IEEE Robotics and Automation Letters , vol. 7 , no. 4 , pp. 11847 - 11854 . https://doi.org/10.1109/LRA.2022.3207545 |
| سنة النشر: | 2022 |
| المجموعة: | Aarhus University: Research |
| مصطلحات موضوعية: | Aerial systems: perception and autonomy, aerial systems: applications, aerial systems: mechanics and control |
| الوصف: | In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone racing. In this work, we propose a novel deep neural network-based perception method for racing gate detection – PencilNet 1 – which relies on a lightweight neural network backbone on top of a pencil filter. This approach unifies predictions of the gates' 2D position, distance, and orientation in a single pose tuple. We show that our method is effective for zero-shot sim-to-real transfer learning that does not need any real-world training samples. Moreover, our framework is highly robust to illumination changes commonly seen under rapid flight compared to state-of-art methods. A thorough set of experiments demonstrates the effectiveness of this approach in multiple challenging scenarios, where the drone completes various tracks under different lighting conditions. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | https://pure.au.dk/portal/en/publications/0244a933-3005-48fd-8b62-76b88bf2935d |
| DOI: | 10.1109/LRA.2022.3207545 |
| الاتاحة: | https://pure.au.dk/portal/en/publications/0244a933-3005-48fd-8b62-76b88bf2935d https://doi.org/10.1109/LRA.2022.3207545 https://arxiv.org/pdf/2207.14131 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.EFB6AC7C |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/LRA.2022.3207545 |
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