LAUSR

Inspired by simultaneous localization and mapping (SLAM) style workflow, this article presented an online sequential structure from motion (SfM) solution for high-frequency video and large baseline high-resolution aerial images with high efficiency and novel precision. First, as traditional SLAM systems are not good in processing low overlap images, based on our novel hierarchical feature matching paradigm with multihomography and BoW, we proposed a robust tracking method where the relative pose and its scale are estimated separately followed by a joint optimization by considering both perspective-n-point (PnP) and epipolar constraints. Second, to further optimize the camera poses for the sparse map and dense pointcloud reconstruction, we provided a graph-based optimization with reprojection and GPS constraints, which make the camera trajectory and map georeferenced. We also incrementally generated the dense point cloud in real time from keyframes after local mapping optimization. Finally, we use a publicly available aerial image dataset with sequences of different environments, to evaluate the effectiveness of the proposed method, meanwhile, the robust performance of our solution is demonstrated with applications of high-quality aerial images mosaic and digital surface model (DSM) reconstruction in real time. Compared with the state-of-the-art SLAM and traditional SfM methods, the presented system can output large-scale high-quality ortho-mosaic and DSM in real time with the low computational cost.