Abstract
Autonomous drone racing (ADR) demands state estimation that is simultaneously computationally efficient and resilient to the perceptual degradation experienced during extreme velocity and maneuvers. Traditional frameworks typically rely on conventional visual-inertial pipelines with loosely-coupled gate-based Perspective-n-Points (PnP) corrections that suffer from a rigid requirement for four visible features and information loss in intermediate steps. Furthermore, the absence of GNSS and Motion Capture systems in uninstrumented, competitive racing environments makes the objective evaluation of such systems remarkably difficult. To address these limitations, we propose ADR-VINS, a robust, monocular visual-inertial state estimation framework based on an Error-State Kalman Filter (ESKF) tailored for autonomous drone racing. Our approach integrates direct pixel reprojection errors from gate corners features as innovation terms within the filter. By bypassing intermediate PnP solvers, ADR-VINS maintains valid state updates with as few as two visible corners and utilizes robust reweighting instead of RANSAC-based schemes to handle outliers, enhancing computational efficiency. Furthermore, we introduce ADR-FGO, an offline Factor-Graph Optimization framework to generate high-fidelity reference trajectories that facilitate post-flight performance evaluation and analysis on uninstrumented, GNSS-denied environments. The proposed system is validated using TII-RATM dataset, where ADR-VINS achieves an average RMS translation error of 0.134 m, while ADR-FGO yields 0.060 m as a smoothing-based reference. Finally, ADR-VINS was successfully deployed in the A2RL Drone Championship Season 2, maintaining stable and robust estimation despite noisy detections during high-agility flight at top speeds of 20.9 m/s. We further utilize ADR-FGO for post-flight evaluation in uninstrumented racing environments.
Abstract (translated)
自主无人机竞速（ADR）需要一种同时计算效率高且能抵抗高速度和机动过程中感知退化影响的状态估计方法。传统框架通常依赖于传统的视觉惯性管道，这些管道采用松耦合的门基Perspective-n-Points (PnP) 校正，这种校正方式严格要求四个可见特征，并在中间步骤中丢失信息。此外，在没有GNSS和运动捕捉系统的非仪器化竞争环境中，这样的系统的目标评估非常困难。 为了解决这些问题，我们提出了ADR-VINS，这是一个基于误差状态卡尔曼滤波器（ESKF）的稳健单目视觉惯性状态估计框架，专为自主无人机竞速设计。我们的方法将直接像素重投影误差从门角特征作为创新项整合到滤波器中。通过绕过中间的PnP求解器，ADR-VINS即使在只有两个可见角落的情况下也能保持有效状态更新，并且采用鲁棒重新加权方案处理离群值，而不是基于RANSAC的方法，这提高了计算效率。 此外，我们还引入了ADR-FGO，这是一个离线因子图优化框架，用于生成高保真参考轨迹，在非仪器化、无GNSS的环境中进行飞行后的性能评估和分析。该系统在TII-RATM数据集上得到了验证，其中ADR-VINS实现了平均0.134米的RMS平移误差，而ADR-FGO作为基于平滑的参考轨迹生成了0.060米的结果。 最后，在A2RL无人机锦标赛第二季中，ADR-VINS在最高飞行速度达到20.9m/s的情况下成功部署，并且即使检测噪声大也能保持稳定和稳健的状态估计。我们进一步使用ADR-FGO进行非仪器化竞速环境中的飞行后评估。
URL
https://arxiv.org/abs/2603.02742
