Automated Relative Orientation of UAV-Based Imagery in the Presence of Prior Information for the Flight Trajectory

Abstract uav -based 3D reconstruction has been used in various applications. However, mitigating the impact of outliers in automatically matched points remains to be a challenging task. Assuming the availability of prior information regarding the uav trajectory, this paper presents two approaches for reliable estimation of Relative Orientation Parameters ( rop s) in the presence of high percentage of matching outliers. The first approach, which assumes that the uav platform is moving at a constant flying height while maintaining the camera in a nadirlooking orientation, provides a two-point closed-form solution. The second approach starts from prior information regarding the flight trajectory to define a linearized model, which is augmented with a built-in outlier removal procedure, to estimate a refined set of rop s. Experimental results from real datasets demonstrate the feasibility of the proposed approaches in providing reliable rop s from uav -based imagery in the presence of a high percentage of matching outliers (up to 90 percent).