Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which can detect the gate at 20HZ on a Parrot Bebop drone. Then, with the gate detection result, we developed a robust pose estimation algorithm which has better tolerance to detection noise than a state-of-the-art perspective-n-point method. During the race, sometimes the gates are not in the drone's field of view. For this case, a state prediction-based feed-forward control strategy is developed to steer the drone to fly to the next gate. Experiments show that the drone can fly a half-circle with 1.5m radius within 2 seconds with only 30cm error at the end of the circle without any position feedback. Finally, the whole system is tested in a complex environment (a showroom in the faculty of Aerospace Engineering, TU Delft). The result shows that the drone can complete the track of 15 gates with a speed of 1.5m/s which is faster than the speeds exhibited at the 2016 and 2017 IROS autonomous drone races.

中文翻译：

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自主无人机竞赛：一种计算效率高的基于视觉的导航和控制策略

无人机赛车正在成为一项流行的运动，人类飞行员必须控制他们的无人机在复杂的环境中高速飞行，并按照预先定义的顺序通过多个登机口。在本文中，我们为无人机开发了一个自主系统，可以仅使用机载资源完全自主地比赛。我们开发了用于视觉导航的高效蛇门检测算法，而不是常用的视觉导航方法，例如同时定位和映射以及视觉惯性里程计，这些方法对微型飞行器 (MAV) 的计算成本很高，它可以在Parrot Bebop 无人机上的 20HZ。然后，根据门检测结果，我们开发了一种鲁棒的姿态估计算法，它比最先进的透视 n 点方法对检测噪声具有更好的耐受性。在比赛期间，有时大门不在无人机的视野内。对于这种情况，开发了一种基于状态预测的前馈控制策略来引导无人机飞到下一个登机口。实验表明，无人机可以在2秒内飞出半径1.5m的半圆，圆的末端误差只有30cm，没有任何位置反馈。最后，整个系统在复杂的环境（代尔夫特理工大学航空航天工程学院的陈列室）中进行测试。结果表明，该无人机可以以1.5m/s的速度完成15个门的轨迹，比2016年和2017年IROS自主无人机比赛中展示的速度更快。实验表明，无人机可以在2秒内飞出半径1.5m的半圆，圆的末端误差只有30cm，没有任何位置反馈。最后，整个系统在复杂的环境（代尔夫特理工大学航空航天工程学院的陈列室）中进行测试。结果表明，该无人机可以以1.5m/s的速度完成15个门的轨迹，比2016年和2017年IROS自主无人机比赛中展示的速度更快。实验表明，无人机可以在2秒内飞出半径1.5m的半圆，圆的末端误差只有30cm，没有任何位置反馈。最后，整个系统在复杂的环境（代尔夫特理工大学航空航天工程学院的陈列室）中进行测试。结果表明，该无人机可以以1.5m/s的速度完成15个门的轨迹，比2016年和2017年IROS自主无人机比赛中展示的速度更快。