MonoRace: 通过鲁棒的单目AI赢得冠军级别的无人机竞速
MonoRace: Winning Champion-Level Drone Racing with Robust Monocular AI
AI总结：
MonoRace通过单目AI和鲁棒状态估计，在无外部跟踪系统下实现冠军级无人机竞速，以500Hz运行神经网络，超越所有AI和人类冠军。
摘要：
自主无人机竞速代表了机器人研究中的一个重要前沿。它需要一种能够在轻量飞行机器人上运行的AI，以在严格资源和时间限制下运行，同时将物理系统推至极限。该领域的最新技术是一个配备立体相机和惯性测量单元（IMU）的系统，能够在受控的室内环境中击败人类无人机竞速冠军。本文提出MonoRace：一种机载无人机竞速方法，使用单目滚动快门相机和IMU，能够在没有外部运动跟踪系统的情况下适应竞赛环境。该方法的特点是鲁棒的状态估计，结合基于神经网络的门段隔离和无人机模型。此外，它还包括一个离线优化过程，利用已知的门几何形状来细化任何状态估计参数。该离线优化仅基于机载飞行数据，并且对于微调关键的外部相机校准参数至关重要。此外，引导和控制由一个神经网络执行，该网络通过直接发送电机命令来省略内环控制器。这个小型网络以500Hz的速度在飞行控制器上运行。所提出的方法赢得了2025年阿布扎赫尔自主无人机竞速比赛（A2RL），在直接淘汰赛中击败了所有竞争AI团队和三位人类世界冠军飞行员。它在自主无人机竞速研究中设立了新的里程碑，达到了每小时100公里的速度，并成功应对了诸如相机干扰和IMU饱和等问题。
英文摘要：
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 estimation parameter. This offline optimization is based purely on onboard flight data and is important for fine-tuning the vital external camera calibration parameters. Furthermore, the guidance and control are performed by a neural network that foregoes inner loop controllers by directly sending motor commands. This small network runs on the flight controller at 500Hz. The proposed approach won the 2025 Abu Dhabi Autonomous Drone Racing Competition (A2RL), outperforming all competing AI teams and three human world champion pilots in a direct knockout tournament. It set a new milestone in autonomous drone racing research, reaching speeds up to 100 km/h on the competition track and successfully coping with problems such as camera interference and IMU saturation.
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。
