基于视觉的位姿估计的保护水平
Protection Levels for Vision-Based Pose Estimation
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中文总结 AI 辅助
本研究扩展了现有视觉位姿估计框架，推导了适用于航空场景的非线性PnP问题保护水平算法，分析了相关因素对其的影响并展示了权衡关系，为视觉导航的完整性认证提供支持。
中文摘要 AI 辅助
视觉导航可补充全球导航卫星系统，但认证要求需考虑故障测量的完整性保证。此前研究提出了一种受接收机自主完整性监测启发的、用于基于跑道的位姿估计的概率计算机视觉流水线，具备故障检测能力。本研究扩展了该框架，推导了保护水平，该保护水平提供了在未检测到故障下仍有效的位姿误差概率边界。我们提出了一种用于计算非线性PnP（Perspective-n-Point，透视n点）问题保护水平的算法，适用于航空场景，该算法直接覆盖飞机位姿的全部6个自由度（位置与姿态）。我们分析了测量冗余度、像素级预测不确定性以及跑道距离对所得保护水平的影响，为使结果直观，我们在一个示例跑道案例中展示了保护水平的权衡关系。
英文摘要
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.
