Abstract
Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.
Highlights
The problem of external orientation estimation is one of the central problems in photogrammetry and computer vision
In computer vision society this problem is sometimes referenced as Perspective-n-Point (PnP) problem, where n represent the number of available reference points
The resulting system of equations could be solved to obtain a single polynomial in one variable of degree two that could be solved for the unknown rotation angle. Such approach was proved to be robust against small errors in gravity vector direction and errors in reference point positions up to 1 pixel
Summary
The problem of external orientation estimation is one of the central problems in photogrammetry and computer vision. It could be stated as an estimation of six parameters of the external orientation that define the spatial position and orientation of the camera coordinate system with respect to the global object coordinate system (Luhmann et al, 2014). This problem is commonly known as the problem of camera calibration. In computer vision society this problem is sometimes referenced as Perspective-n-Point (PnP) problem, where n represent the number of available reference points
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More From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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