Abstract
Orthophoto generation is a popular topic in aerial photogrammetry and 3D reconstruction. It is generally computationally expensive with large memory consumption. Inspired by the simultaneous localization and mapping (SLAM) workflow, this paper presents an online sequential orthophoto mosaicing solution for large baseline high-resolution aerial images with high efficiency and novel precision. An appearance and spatial correlation-constrained fast low-overlap neighbor candidate query and matching strategy is used for efficient and robust global matching. Instead of estimating 3D positions of sparse mappoints, which is outlier sensitive, we propose to describe the ground reconstruction with multiple stitching planes, where parameters are reduced for fast nonconvex graph optimization. GPS information is also fused along with six degrees of freedom (6-DOF) pose estimation, which not only provides georeferenced coordinates, but also converges property and robustness. An incremental orthophoto is generated by fusing the latest images with adaptive weighted multiband algorithm, and all results are tiled with level of detail (LoD) support for efficient rendering and further disk cache for reducing memory usages. Public datasets are evaluated by comparing state-of-the-art software. Results show that our system outputs orthophoto with novel efficiency, quality, and robustness in real-time. An android commercial application is developed for online stitching with DJIdrones, considering the excellent performance of our algorithm.
Highlights
Aerial image mosaicing has been used in many scenes, such as farmland mosaicing, forest fire detection, post-disaster relief, and military reconnaissance
The task of aerial image mosaicing can be implemented in two ways
First is offline mosaicing [1,2,3], where the mosaicing process is usually applied after obtaining all the image data of the target area with the unmanned aerial vehicles (UAVs)
Summary
Aerial image mosaicing has been used in many scenes, such as farmland mosaicing, forest fire detection, post-disaster relief, and military reconnaissance. First is offline mosaicing [1,2,3], where the mosaicing process is usually applied after obtaining all the image data of the target area with the unmanned aerial vehicles (UAVs). This approach could provide integrated information for image mapping. On the basis of the estimated camera poses, the second method is online mosaicing, which stitches images in real-time [4,5,6] This approach is necessary in some specific application scenarios such as live map visualization through virtual reality [7,8,9]
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