In this paper we introduce a vision-aided navigation (VAN) pipeline designed to support ground navigation of autonomous aircraft. The proposed algorithm combines the computational efficiency of indirect methods with the robustness of direct image-based techniques to enhance solution integrity. The pipeline starts by processing ground images (e.g., acquired by a taxiing aircraft) and relates them via a feature-based structure-from-motion (SfM) solution. A ground plane mosaic is then constructed via homography transforms and matched to satellite imagery using a sum of squares differences (SSD) of intensities. Experimental results reveal that drift within the SfM solution, similar to that observed in dead-reckoning systems, challenges the expected accuracy benefits of map-matching with a wide-baseline ground-plane mosaic. However, the proposed algorithm demonstrates key integrity features, such as the ability to identify registration anomalies and ambiguous matches. These characteristics of the pipeline can mitigate outlier behaviors and contribute toward a robust, certifiable solution for autonomous surface movement of aircraft.

翻译：本文提出了一种旨在支持自主航空器地面导航的视觉辅助导航（VAN）流程。所提出的算法结合了间接方法的计算效率与基于直接图像技术的鲁棒性，以增强解的完整性。该流程首先处理地面图像（例如，由滑行中的航空器获取），并通过基于特征的运动恢复结构（SfM）解算建立图像间的关联。随后，通过单应性变换构建地面平面拼接图，并利用强度平方差和（SSD）将其与卫星图像进行匹配。实验结果表明，SfM解算中的漂移（类似于航位推算系统中观察到的现象）对利用宽基线地面平面拼接图进行地图匹配所期望的精度优势构成了挑战。然而，所提出的算法展示了关键的完整性特征，例如能够识别配准异常和模糊匹配。流程的这些特性可以减轻异常行为的影响，并为航空器自主地面运动提供一个稳健且可认证的解决方案。