SCAR: Satellite Imagery-Based Calibration for Aerial Recordings

TL;DR

SCAR is a novel method that uses satellite imagery to automatically refine and maintain calibration of aerial visual-inertial systems over long periods, improving accuracy without manual effort.

Contribution

SCAR introduces a satellite imagery-based calibration approach that automatically corrects calibration drift in aerial systems during field deployment.

Findings

SCAR reduces median reprojection error significantly.

SCAR improves visual localization rotation errors.

SCAR maintains calibration accuracy over two years.

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.