仅用单目相机和惯性传感器实现高速无人机精准定位，无需外部设备。
Vision-only UAV State Estimation for Fast Flights Without External Localization Systems: A2RL Drone Racing Finalist Approach
- 融合视觉-惯性里程计、地标测量与惯性数据，构建闭环校正系统。
- 在1600次仿真和真实飞行中验证，大幅降低高速机动时的定位漂移。
- 适合竞赛级无人机或无卫星信号环境下的自主飞行系统使用。
在无卫星信号的复杂环境中实现高速飞行与剧烈机动，要求无人机具备快速、可靠且精确的状态估计能力。本文提出一种基于单目RGB相机与惯性测量单元（IMU）的机载状态估计算法。该方法融合视觉-惯性里程计（VIO）、机载地标感知系统及IMU数据，生成高精度状态估计。通过构建新颖的数学漂移模型，利用机载测量数据对VIO的漂移进行实时估计与补偿，修正其位置、姿态、线速度与角速度。现有先进方法常依赖更复杂的硬件（如双目相机或测距仪），且未校正VIO的漂移状态，导致高速机动时出现误差。本方法可全面纠正所有VIO状态，实现动态剧烈运动下的准确估计。研究通过1600次仿真和大量真实实验充分验证。此外，该方法应用于A2RL无人机竞速挑战赛2025，从210支队伍中脱颖而出，进入决赛四强并获得奖牌。
原文摘要 · Abstract (English)
Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular RGB camera and an IMU. Our approach fuses data from Visual-Inertial Odometry (VIO), an onboard landmark-based camera measurement system, and an IMU to produce an accurate state estimate. Using onboard measurement data, we estimate and compensate for VIO drift through a novel mathematical drift model. State-of-the-art approaches often rely on more complex hardware (e.g., stereo cameras or rangefinders) and use uncorrected drifting VIO velocities, orientation, and angular rates, leading to errors during fast maneuvers. In contrast, our method corrects all VIO states (position, orientation, linear and angular velocity), resulting in accurate state estimation even during rapid and dynamic motion. Our approach was thoroughly validated through 1600 simulations and numerous real-world experiments. Furthermore, we applied the proposed method in the A2RL Drone Racing Challenge 2025, where our team advanced to the final four out of 210 teams and earned a medal.
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