仅视觉的无人机快速飞行状态估计：无需外部定位系统：A2RL无人机赛车决赛方法
Vision-only UAV State Estimation for Fast Flights Without External Localization Systems: A2RL Drone Racing Finalist Approach
AI总结：
本文提出了一种基于视觉和IMU的无人机快速飞行状态估计方法，通过修正VIO漂移实现高精度状态估计，并在A2RL无人机赛车挑战中取得优异成绩。
摘要：
在密集的GNSS拒绝环境中进行快速飞行并执行激进动作需要快速、可靠且准确的无人机状态估计。本文提出了一种利用单目RGB相机和IMU进行高速无人机 onboard 状态估计的方法。我们的方法融合了视觉惯性里程计（VIO）、机载地标基相机测量系统和IMU的数据，以产生准确的状态估计。通过一种新的数学漂移模型，利用机载测量数据，我们估计并补偿VIO漂移。最先进的方法往往依赖更复杂的硬件（例如立体相机或测距仪），并使用未经修正的漂移VIO速度、方向和角速度，导致在快速动作中出现误差。相比之下，我们的方法修正了所有VIO状态（位置、方向、线速度和角速度），即使在快速动态运动中也能实现准确的状态估计。我们的方法通过1600次模拟和大量现实世界实验进行了彻底验证。此外，我们还将在A2RL无人机赛车挑战2025中应用所提出的方法，我们的团队在210支队伍中进入了前四名并获得奖牌。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
