Efficient Large-Scale Orthomosaicking: Integrating Learning-Based Visual Odometry With GNSS for Real-Time Consistency

While traditional oblique photogrammetry offers high-fidelity accuracy, it suffers from significant processing latency, making it less suitable for time-sensitive applications such as emergency response. Real-time incremental aerial mapping, which prioritizes immediate visualization, faces key challenges including the efficiency–accuracy trade-off, long-range drift, and appearance inconsistency under resource constraints and illumination variation. To address these issues, we propose a novel real-time large-scale orthomosaicking system that integrate learning-based VO(visual odometry) with GNSS(global navigation satellite system) via hierarchical alignment for efficient aerial orthomosaic generation. By combining robust hybrid VO with tightly-coupled optimization of GNSS-derived relative transforms, our method effectively mitigates long-range drift in large-scale environments. Furthermore, we introduce a semantic-aware coarse-to-fine mosaic plane fitting and a dual orthorectification strategy to adaptively balances local accuracy and global geomatric consistency. Finally, the multi-band Laplacian blending is employed to ensure seamless appearance transitions. Experimental results demonstrate that our system outperforms commercial solutions in both speed and consistency, particularly in complex urban structures, texture-sparse regions, and flight boundary areas. These results confirm that our method is able to deliver superior mapping quality while maintaining real-time performance.