We introduce SCAR, a method for long-term auto-calibration refinement of aerial visual-inertial systems that exploits georeferenced satellite imagery as a persistent global reference. SCAR estimates both intrinsic and extrinsic parameters by aligning aerial images with 2D--3D correspondences derived from publicly available orthophotos and elevation models. In contrast to existing approaches that rely on dedicated calibration maneuvers or manually surveyed ground control points, our method leverages external geospatial data to detect and correct calibration degradation under field deployment conditions. We evaluate our approach on six large-scale aerial campaigns conducted over two years under diverse seasonal and environmental conditions. Across all sequences, SCAR consistently outperforms established baselines (Kalibr, COLMAP, VINS-Mono), reducing median reprojection error by a large margin, and translating these calibration gains into substantially lower visual localization rotation errors and higher pose accuracy. These results demonstrate that SCAR provides accurate, robust, and reproducible calibration over long-term aerial operations without the need for manual intervention.

翻译：我们提出SCAR方法，这是一种利用地理参考卫星影像作为持久性全局参照的航空视觉-惯性系统长期自动标定优化方法。SCAR通过将航空影像与公开正射影像及高程模型生成的2D-3D对应关系进行对齐，同时估计内参和外参参数。与依赖专用标定机动或人工测量地面控制点的现有方法不同，本方法利用外部地理空间数据在实地部署条件下检测并校正标定退化。我们在两年间不同季节和环境条件下开展的六次大规模航空任务中评估了该方法。在所有序列中，SCAR始终优于现有基线方法（Kalibr、COLMAP、VINS-Mono），大幅降低了中值重投影误差，并将这些标定增益转化为显著降低的视觉定位旋转误差和更高的位姿精度。这些结果表明，SCAR能够在无需人工干预的情况下，为长期航空作业提供精确、鲁棒且可复现的标定结果。