🤖 AI Summary
This work addresses the degradation of localization accuracy in visual-inertial systems during long-term aerial deployments, which arises from calibration drift. To mitigate this issue, the authors propose a self-calibration method leveraging publicly available geospatial reference data—specifically, satellite orthoimagery and digital elevation models—as a global geometric prior. By integrating 2D–3D feature matching with joint visual-inertial optimization, the approach automatically refines both intrinsic and extrinsic camera parameters without requiring dedicated calibration maneuvers or manually placed ground control points. Evaluated over six large-scale aerial missions spanning two years, the method consistently outperforms established baselines including Kalibr, COLMAP, and VINS-Mono, significantly reducing reprojection and pose estimation errors while enhancing the long-term accuracy and robustness of the system in real-world operational scenarios.
📝 Abstract
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.