Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.

我们将最先进的规划和控制系统与卷积神经网络相结合，实现了自主机器人在动态环境中的高速飞行和对感知目标的实时认识，无需进行任何调整，同时通过领域随机化生成大量的模拟数据，使我们的系统对光照和目标出现变化具有鲁棒性，实现了在敏捷无人机飞行任务中的零次模拟到实际的成功展示。

深度无人机比赛：从模拟到现实中的域随机化

Deep Drone Racing: From Simulation to Reality with Domain Randomization

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。