Drift‐Corrected Monocular VIO and Perception‐Aware Planning for Autonomous Drone Racing

The Abu Dhabi Autonomous Racing League (A2RL) x Drone Champions League (DCL) competition requires teams to perform high‐speed autonomous drone racing using only a single camera and a low‐quality inertial measurement unit—a minimal sensor set that mirrors expert human drone racing pilots. This sensor limitation makes the system susceptible to drift from Visual‐Inertial Odometry (VIO), particularly during long and fast flights with aggressive maneuvers. This paper presents the system developed for the championship. Our approach corrects VIO drift by fusing its output with global position measurements derived from a YOLO‐based gate detector using a Kalman filter, reducing the translational error by 46% relative to the VIO only. A perception‐aware planner generates trajectories that balance speed with the need to keep gates visible for the perception system, improving gate visibility by 16%. The system demonstrated high performance, securing podium finishes across multiple categories: third place in the AI Grand Challenge with a top speed of 43.2 km/h, second place in the AI Drag Race with over 59 km/h, and second place in the AI Multi‐Drone Race. We detail the complete architecture and present a performance analysis based on experimental data from the competition, contributing our insights into building a successful system for monocular vision‐based autonomous drone flight.