Autonomous drone racing represents a major frontier in robotics research. It requires an Artificial Intelligence (AI) that can run on board light-weight flying robots under tight resource and time constraints, while pushing the physical system to its limits. The state of the art in this area consists of a system with a stereo camera and an inertial measurement unit (IMU) that beat human drone racing champions in a controlled indoor environment. Here, we present MonoRace: an onboard drone racing approach that uses a monocular, rolling-shutter camera and IMU that generalizes to a competition environment without any external motion tracking system. The approach features robust state estimation that combines neural-network-based gate segmentation with a drone model. Moreover, it includes an offline optimization procedure that leverages the known geometry of gates to refine any state estimation parameter. This offline optimization is based purely on onboard flight data and is important for fine-tuning the vital external camera calibration parameters. Furthermore, the guidance and control are performed by a neural network that foregoes inner loop controllers by directly sending motor commands. This small network runs on the flight controller at 500Hz. The proposed approach won the 2025 Abu Dhabi Autonomous Drone Racing Competition (A2RL), outperforming all competing AI teams and three human world champion pilots in a direct knockout tournament. It set a new milestone in autonomous drone racing research, reaching speeds up to 100 km/h on the competition track and successfully coping with problems such as camera interference and IMU saturation.

翻译：自主无人机竞速是机器人研究的一个重要前沿领域。它要求人工智能（AI）能够在资源与时间严格受限的条件下，在轻量级飞行机器人上实时运行，同时将物理系统推向性能极限。该领域的现有最先进系统采用立体相机与惯性测量单元（IMU），已在受控室内环境中击败人类无人机竞速冠军。本文提出 MonoRace：一种仅使用单目卷帘快门相机与 IMU 的机载无人机竞速方法，该方法可推广至无任何外部运动跟踪系统的竞赛环境。该方法的特色在于结合了基于神经网络的闸门分割与无人机模型的鲁棒状态估计。此外，它包含一种离线优化流程，利用已知的闸门几何结构来精调所有状态估计参数。该离线优化完全基于机载飞行数据，对于微调关键的外部相机标定参数至关重要。进一步地，其制导与控制由一个神经网络执行，该网络绕过内环控制器，直接发送电机指令。这个小规模网络在飞行控制器上以 500Hz 的频率运行。所提出的方法在 2025 年阿布扎比自主无人机竞速赛（A2RL）中获胜，在直接淘汰赛中超越了所有参赛的 AI 团队及三位人类世界冠军飞行员。这为自主无人机竞速研究树立了新的里程碑，其在竞赛赛道上的速度最高可达 100 公里/小时，并成功应对了相机干扰与 IMU 饱和等问题。