A Relative Rotation between Two Overlapping UAV's Images

Abstract

In this paper, we study the influence of varying baseline components on the accuracy of a relative rotation between two overlapping aerial images taken form unmanned aerial vehicle (UAV) flight. The case is relevant when mosaicking UAV's aerial images by registering each individual image. Geotagged images facilitated by a navigational grade GPS receiver on board inform the camera position when taking pictures. However, these low accuracies of geographical coordinates encoded in an EXIF format are unreliable to depict baseline vector components between subsequent overlapping images. This research investigates these influences on the stability of rotation elements when the vector components are entered into a standard coplanarity condition equation to determine the relative rotation of the stereo images. Assuming a nadir looking camera on board while the UAV platform is flying at a constant height, the resulted vector directions are utilized to constraint the coplanarity equation. A detailed analysis of each variation is given. Our experiments based on real datasets confirm that the relative rotation between two successive overlapping images is practically unaffected by the accuracy of positioning method. Furthermore, the coplanarity constraint is invariant with respect to a translation along the baseline of the aerial stereo images.

Cadastral Surveys with Non-metric Camera Using Uav: A Feasibility Study

“…Here i and j represent information pertaining to the i ℎ image out of the m images [17,19] and relative orientation methods [20,21], whereas a spatial position of the object space points are calculated by space intersection [22]. The generation of initial approximate values for all parameters is required in (1) because the photogrammetric observation equations are non-linear with respect to the parameters and must be iteratively solved using a linearized set of equations.…”

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“…The self-calibration method is performed by means of the bundle adjustment [6] which provides a simultaneous determination of the interior and exterior orientation parameters, as well as the object point coordinates performed with or without a provision of known coordinates of control points [11], [13], [15], [21]-- [23]. However, while object space control points of known 3D coordinates are available, calibration parameters can be recovered using space resection methods [24]-- [26], relative orientation methods [27], [28], or bundle adjustment with fixed 3D coordinates. Before elaborating the result, a brief discussion about the self-calibration method is following.…”

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