MonoRace: Winning Champion-Level Drone Racing with Robust Monocular AI
Stavrow A. Bahnam, Robin Ferede, Till M. Blaha, Anton E. Lang, Erin Lucassen, Quentin Missinne, Aderik E.C. Verraest, Christophe De Wagter, Guido C.H.E. de Croon

TL;DR
MonoRace introduces a monocular AI system for drone racing that operates onboard with robust state estimation and neural network guidance, achieving champion-level performance without external tracking.
Contribution
The paper presents a novel monocular drone racing approach that generalizes to competition environments, combining neural segmentation, offline optimization, and neural network control for high-speed racing.
Findings
Won the 2025 Abu Dhabi Drone Racing Competition
Outperformed human champions and competing AI teams
Achieved speeds up to 100 km/h in race conditions
Abstract
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…
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Taxonomy
TopicsUAV Applications and Optimization · Aerospace and Aviation Technology · Robotics and Sensor-Based Localization
