论文标题
基于事件的自动无人机赛车导航，具有稀疏的封闭式反复网络
Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network
论文作者
论文摘要
基于事件的视力已经通过有望更快的响应，较低的能量消耗和较低的带宽而彻底改变了机器人的感知任务，而无需引入运动模糊。在这项工作中，提出了一种基于稀疏的卷积来检测赛道中门的新型深度学习方法，它是使用基于事件的自动无人机赛车问题的愿景提出的。我们在真实的机器人平台上展示了感知管道的效率和功效，该机器人平台可以安全地实时浏览典型的自主无人机赛车。在整个实验中，我们表明，基于事件的视觉具有拟议的封闭式复发单元，并在模拟事件数据上预算了模型，可显着提高栅极检测精度。此外，公开发布一个基于事件的无人机赛车数据集，该数据集由模拟和真实数据序列组成。
Event-based vision has already revolutionized the perception task for robots by promising faster response, lower energy consumption, and lower bandwidth without introducing motion blur. In this work, a novel deep learning method based on gated recurrent units utilizing sparse convolutions for detecting gates in a race track is proposed using event-based vision for the autonomous drone racing problem. We demonstrate the efficiency and efficacy of the perception pipeline on a real robot platform that can safely navigate a typical autonomous drone racing track in real-time. Throughout the experiments, we show that the event-based vision with the proposed gated recurrent unit and pretrained models on simulated event data significantly improve the gate detection precision. Furthermore, an event-based drone racing dataset consisting of both simulated and real data sequences is publicly released.
