The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B3
16 Jun 2016
 | 16 Jun 2016

Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

F. He and A. Habib

Keywords: Relative Orientation, Essential Matrix, Closed-form Solution, UAVs

Abstract. Thanks to recent advances at the hardware (e.g., emergence of reliable platforms at low cost) and software (e.g., automated identification of conjugate points in overlapping images) levels, UAV-based 3D reconstruction has been widely used in various applications. However, mitigating the impact of outliers in automatically matched points in UAV imagery, especially when dealing with scenes that has poor and/or repetitive texture, remains to be a challenging task. In spite of the fact that existing literature has already demonstrated that incorporating prior motion information can play an important role in increasing the reliability of the matching process, there is a lack of methodologies that are mainly suited for UAV imagery. Assuming the availability of prior information regarding the trajectory of a UAV-platform, this paper presents a two-point approach for reliable estimation of Relative Orientation Parameters (ROPs) of UAV-based images. This approach is based on the assumption that the UAV platform is moving at a constant flying height while maintaining the camera in a nadir-looking orientation. For this flight scenario, a closed-form solution that can be derived using a minimum of two pairs of conjugate points is established. In order to evaluate the performance of the proposed approach, experimental tests using real stereo-pairs acquired from different UAV platforms have been conducted. The derived results from the comparative performance analysis against the Nistér five-point approach demonstrate that the proposed two-point approach is capable of providing reliable estimate of the ROPs from UAV-based imagery in the presence of poor and/or repetitive texture with high percentage of matching outliers.