Robust Camera Pose Refinement and Rapid SfM for Multiview Aerial Imagery—Without RANSAC

A robust camera pose refinement approach for sequential wide-area airborne imagery is proposed in this letter. Image frames are sequentially acquired, and with each frame, its corresponding position and orientation are approximately available from airborne platform inertial measurement unit and GPS sensors. In the proposed structure from motion (SfM) approach the available approximation of camera parameters (from low-certainty sensors) is directly used in an optimization stage. The putative matches obtained from the sequential matching paradigm are also directly used in the optimization with no early stage filtering (e.g., no RANSAC). A robust function is proposed and used to deal with outliers (mismatches). The full pipeline has been run over a set of wide-area motion imagery data collected by an airplane flying over different cities in the U.S. The results prove the power and efficiency of the proposed pipeline. Effectiveness of the proposed robust function is compared with some popular robust functions such as Cauchy and Huber using synthetic data.