SCAR: Satellite Imagery-Based Calibration for Aerial Recordings
This addresses the problem of calibration degradation in aerial systems for field deployment without manual intervention, representing a novel method for a known bottleneck.
The authors tackled the problem of long-term auto-calibration refinement for aerial visual-inertial systems by introducing SCAR, which uses georeferenced satellite imagery as a persistent global reference. The method reduced median reprojection error by a large margin across six large-scale aerial campaigns over two years, leading to substantially lower visual localization rotation errors and higher pose accuracy compared to established baselines.
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.