A new relative orientation w local parameter optimization method of the essential matrix for the large scale close range photogrammetry is presented in this paper to improve the accuracy and stability of the measurement system. For the matched images, according to the closed-loop polynomial algorithm, the essential matrix is initialized, and an iterative algorithm based on local parameter optimization is proposed. Then the relative exterior orientation parameters are solved from the essential matrix, and only one correct solution is determined by the Cheirality constraints. The orientation experiment of the expandable truss microwave antenna profile measurement is carried out to verify the accuracy and reliability of the new method. Compared with the traditional methods, this new method has minimum projection error and the least iterations, and it will play a key role in the performance improvement of the whole system.
In close range photogrammetry and vision metrology, several images which are taken at different stations are required
for high accuracy. Before camera calibration and 3D reconstruction, the targets in the images must be recognized and
located with high accuracy firstly. Furthermore, in order to monitor the deformation of the surface, real-time and on-line
photogrammetry system is needed, in which high speed is necessary. So, the image processing method and speed will
affect the accuracy and speed of the photogrammetry system. This paper describes a fast target location method for the
photogrammetry system. Experimental results show that the target edge pixels preserve the important geometric
information for subpixel centroid, which can reach accuracies to 2-3% of the pixel size. The process time of an image
with 3008x2000 pixels is about 0.1S, much higher than other similar methods.
In photogrammetry system, the Base-Distance Ratio, the Image Scale, and the Image Standard Error, which construct the
network strength of the system, are the main accuracy factors. In this paper, the normal and convergent network
configurations of the photogrammetry are studied and the Network Strength, which presents the strength and accuracy of
the camera station network, is expressed with the accuracy factors mentioned above. In order to verify the validation of
this expression, the large-scale 3D reference field is designed and used to test the effects of these accuracy factors. The
experimental results show that the relationship between the accuracy and the factors is consistent with the expression.
These conclusions will guide the photogrammetric work to reduce the system errors.
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