The Abu Dhabi Autonomous Racing League(A2RL) x Drone Champions League competition(DCL) 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, which achieved a competitive performance. Our approach corrected VIO drift by fusing its output with global position measurements derived from a YOLO-based gate detector using a Kalman filter. A perception-aware planner generated trajectories that balance speed with the need to keep gates visible for the perception system. The system demonstrated high performance, securing podium finishes across multiple categories: third place in the AI Grand Challenge with 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 on building a successful system for monocular vision-based autonomous drone flight.

翻译：阿布扎比自主竞速联盟(A2RL)与无人机冠军联赛(DCL)竞赛要求参赛队伍仅使用单目摄像头和低质量惯性测量单元完成高速自主无人机竞速——这套最小传感器配置与人类顶尖竞速飞手的装备相一致。该传感器限制使得系统容易受到视觉惯性里程计(VIO)漂移的影响，尤其在长时间高速飞行和执行剧烈机动时更为显著。本文介绍了为此次锦标赛开发的系统，该系统取得了具有竞争力的表现。我们的方法通过卡尔曼滤波器将VIO输出与基于YOLO的闸门检测器获取的全局位置测量值进行融合，从而校正了VIO漂移。感知感知规划器生成的轨迹在飞行速度与保持闸门对感知系统可见性之间实现了平衡。该系统表现出优异性能，在多个竞赛项目中均登上领奖台：以最高时速43.2公里/小时获得AI大挑战赛第三名，以超过59公里/小时的速度获得AI直线竞速赛第二名，并在AI多机竞速赛中取得第二名。我们详细阐述了完整系统架构，并基于竞赛实验数据进行了性能分析，为构建基于单目视觉的自主无人机飞行系统提供了重要见解。