为航空视觉定位设计概率误差上限，确保故障下仍可靠。
Protection Levels for Vision-Based Pose Estimation
- 基于非线性透视n点问题，直接计算六自由度姿态误差上限。
- 测量冗余越高、像素预测越准、跑道越远，保护水平越优。
- 适用于民航导航系统认证，对安全敏感场景特别有用。
基于视觉的导航可弥补全球卫星导航系统的不足，但认证要求提供故障情况下的完整性保障。以往工作提出了受接收机自主完好性监测启发的概率计算机视觉流程，用于跑道基准位姿估计并具备故障检测能力。本文进一步扩展该框架，推导出保护水平——在未检测到故障时仍有效的姿态误差概率上界。提出一种针对航空应用场景的非线性透视n点问题的保护水平计算算法，直接覆盖飞机姿态的全部六个自由度（位置与朝向）。分析了测量冗余、像素级预测不确定性以及跑道距离对保护水平的影响。通过一个示意性跑道案例展示了保护水平在不同条件下的权衡关系。
原文摘要 · Abstract (English)
Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-$n$-Point problem applied to an aviation setting. The algorithm covers all six degrees of freedom of the aircraft pose (position and orientation) directly. We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels. To make the results tangible, we demonstrate tradeoffs in the protection levels on an illustrative runway example.
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