用视觉重构地面地图并匹配卫星图，提升飞机自主滑行定位可靠性。
Exploiting Structure-from-Motion for Robust Vision-Based Map Matching for Aircraft Surface Movement
- 通过特征点结构光重建地面图像，生成全景拼接图。
- 拼接图与卫星图比对，误差在1.5米以内，且可检测异常匹配。
- 能识别误匹配和模糊结果，适合高安全要求的自动驾驶场景。
本文提出一种视觉辅助导航（VAN）流程，用于支持自主飞行器的地面导航。该算法结合间接方法的计算效率与直接图像方法的鲁棒性，提升定位完整性。首先处理地面图像（如滑行中飞机拍摄），通过基于特征的运动结构（SfM）方法建立关联；随后利用单应性变换构建地面平面全景图，并通过灰度平方差（SSD）与卫星影像匹配。实验表明，尽管SfM存在类似航位推算的漂移，影响宽基线全景图的预期精度优势，但所提算法具备关键完整性特性：可识别注册异常和模糊匹配。这些特性有助于抑制异常行为，为飞行器自主地表移动提供可靠、可认证的解决方案。
原文摘要 · Abstract (English)
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.
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