MonoRace: Robust monocular AI for champion-level drone racing

Autonomous drone racing represents a major frontier in robotics research. It requires an artificial intelligence (AI) that can run onboard lightweight 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 generalizes to a competition environment without any external motion tracking system, using only a monocular rolling-shutter camera and an IMU. 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 extrinsic camera calibration parameters. Furthermore, the guidance and control are performed by a neural network that forgoes inner-loop controllers by directly sending motor commands. This small network runs on the flight controller at 500 hertz. The proposed approach won the Abu Dhabi Autonomous Drone Racing Competition 2025, outperforming all competing autonomous drone race teams and three world champion human pilots in a direct knockout tournament. It reached speeds up to 100 kilometers per hour on the competition track and successfully coped with problems such as camera interference and IMU saturation.