Drift-aware and correction on-the-fly: Airborne LiDAR and RGB cameras online calibration in natural environments

This paper presents an advanced method for the reliable detection of failures and online calibration of airborne LiDARs and cameras in photogrammetric mapping of remote sensing imagery capture scenarios. Traditional calibration methods without targets typically depend on aligning the spatial structures of LiDAR data with texture features, but acquiring dense LiDAR data for matching and optimization in a short amount of time is challenging in real-time operations. In response to this issue, this paper proposes an image feature encoding method designed to dynamically guide the matching of LiDAR feature points, while simultaneously employing context consistency estimation for additional optimization. Importantly, our method can perceive and recalibrate extrinsic errors in real-time scenarios within natural environments, thereby enhancing the robustness of the calibration. The effectiveness and adaptability of our method are demonstrated using our unique airborne LiDAR-based LIVOX-Airborne datasets, which set a new benchmark in both numerical variability and pixel discrepancy, showcasing a variance of 5 . 05 x 10 -4 and a mean error of 0.86 pixels.