Abstract
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.
Abstract (translated)
自主无人机竞速代表了机器人研究的一个重要前沿。它需要能够在资源和时间受限的条件下运行于轻量级飞行机器人上的人工智能，同时将物理系统推向极限。该领域的最新技术包括一个配备了立体摄像头和惯性测量单元（IMU）的系统，在受控室内环境中击败了人类无人机竞速冠军。在这里，我们介绍了MonoRace：一种使用单目滚动快门相机和IMU、适用于无外部运动跟踪系统的竞争环境中的机载无人机竞速方法。这种方法的特点是结合神经网络栅栏分割与无人机模型的稳健状态估计，并且包括一个离线优化过程，该过程利用已知的栅栏几何形状来微调任何状态估算参数。此离线优化完全基于飞行数据，对于精细调整重要的外部相机校准参数至关重要。 此外，引导和控制由神经网络执行，该网络通过直接发送电机命令而跳过了内部回路控制器。这个小型网络在飞行控制器上以每秒500赫兹的频率运行。所提出的方法赢得了2025年阿布扎比自主无人机竞速比赛（A2RL），超过了所有参赛的人工智能团队和三名世界冠军飞行员，后者在直接淘汰赛中被击败。它在自主无人机竞速研究方面设立了新的里程碑，在竞赛赛道上达到了高达100公里/小时的速度，并成功应对了诸如相机干扰和IMU饱和等问题。
URL
https://arxiv.org/abs/2601.15222
