There is a significant difference in correlated color temperature between deep-space environmental light sources and that on the ground. The lack of accurate calibration parameters for cameras can lead to errors in the collected scientific image data, resulting in the failure of scientific tasks. Thus it is of vital significance to accurate white balance calibration for deep-space-exploration cameras. However, traditional white-balance methods have certain limitations. The generalization ability of the white balance correction matrix is insufficient, the ground calibration methods have poor on-orbit adaptability, and deep space exploration targets are unknown and uncertain. To this end, we proposed an on-orbit white balance calibration method for deep-space-exploration cameras based on correlated correlated color temperature interpolation. Aiming to the deep space environment background, four pre-calibrated illuminations with different correlated color temperatures are selected reasonably. Before solving the white balance calibration matrix, we stretch the image grayscale in different exposure times to ensure the brightness balance and obtain the optimal white balance matrix, which improves the generalization ability of the matrix. Finally, by interpolating the pre-calibration white balance matrixes based on the on-orbit environmental correlated color temperature, the average color difference can be reduced by 28 (72.54%) DE2000 color difference units.
Aiming at the problem of low calibration accuracy of the telephoto camera, a telephoto camera calibration method based on a highly robust homography matrix is proposed. In the long-distance and long-focus calibration scenario, this paper expounded and analyzed the reasons for the low calibration accuracy of the telephoto camera, and simulated and compared the influence of optimizing the distortion coefficient on the intrinsic parameters calculation in Zhang's calibration method and staged calibration method , to illustrate the importance of the precision of the homography matrix. To improve the accuracy of the homography matrix, a new optimization loss function under multiple constraints is proposed. Experiments show that the proposed telephoto camera calibration method is reasonable and effective. Compared with the traditional two-dimensional plane calibration method, our method has better calibration accuracy and robustness. When the image is slightly distorted and the noise level = 0.5pixel, the average reprojection error reaches 0.2488 pixel, and the error is reduced by 3.86%.
The asymmetric spatial heterodyne spectral velocimetry technology is a new high-precision velocimetry method proposed in recent decades, and relevant research institutions have also obtained excellent measurement results at the application level based on this technique. However, past researchers mainly focused on the optimization of its optical characteristics and structure, and without in-depth research on its signal model. This paper analyzed the technique from the principal level, established the relevant mathematical model, and explained the origin of its high resolution. Based on the measurement mechanism described in this paper, on the one hand, it reflects the advantages of asymmetric spatial heterodyne spectral velocimetry technology; on the other hand, it also defines its application scope and defects. Through the research in this paper, the asymmetric spatial heterodyne spectral velocimetry technology can have both ultra-high measurement accuracy and low computational complexity. Furthermore, it also provided a reference for its application range.
This paper presents an iterative pose estimation method on the basis of point correspondences, which are composed of
3D coordinates of feature points under object reference frame and their 2D projective coordinates under image reference
frame. The proposed method decomposes the pose estimation into two steps. Firstly, the 3D coordinates of the feature
points under camera reference frame are estimated iteratively by Gauss-Newton method. In this process, the variables are
defined by the lengths of the vectors from the focus point of camera to the feature points; meanwhile, several novel
constraints are constructed by a set of error functions built out of the inner angles and areas of the triangles formed by
three arbitrary non-collinear feature points, because they can describe the shape of object uniquely and completely.
Secondly, by using Gauss-Newton method again, the rotation angles (i.e., pitch, yaw, and roll) and 3D translation of the
object are estimated from the 3D coordinates of the feature points under camera reference frame obtained in the first
step. Experiments involving synthetic data as well as real data indicate that the proposed method is more accurate and no
less fast than the previous method.
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