Autonomous agile flight brings up fundamental challenges in robotics, such as
coping with unreliable state estimation, reacting optimally to dynamically
changing environments, and coupling perception and action in real time under
severe resource constraints. In this paper, we consider these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. Our
approach combines a convolutional neural network (CNN) with a state-of-the-art
path-planning and control system. The CNN directly maps raw images into a
robust representation in the form of a waypoint and desired speed. This
information is then used by the planner to generate a short, minimum-jerk
trajectory segment and corresponding motor commands to reach the desired goal.
We demonstrate our method in autonomous agile flight scenarios, in which a
vision-based quadrotor traverses drone-racing tracks with possibly moving
gates. Our method does not require any explicit map of the environment and runs
fully onboard. We extensively test the precision and robustness of the approach
in simulation and in the physical world. We also evaluate our method against
state-of-the-art navigation approaches and professional human drone pilots.

利用卷积神经网络直接映射原始图像到航点和所需速度的强鲁棒性生成，使用路径规划和控制系统生成短的、最小时间轨迹段，测试表明该方法能够在动态环境中准确和稳健地实现无人机的自主飞行，不需要昂贵的硬件和昂贵的图像处理，且比专业的人类驾驶员更加高效。