The 3D similarity coordinate transformation is widely used to estimate the transformation parameters for measurement datum transformation. Accurate and reliable transformation parameters are crucial for accurate and reliable data integration. However, the accuracy of the transformation parameters can be significantly affected or even severely distorted when the observed coordinates are contaminated by gross errors. To address this problem, an advanced iteratively weighted least squares (IWLS) solution based on the weighted least squares (WLS) is proposed. This solution utilizes the singular value decomposition (SVD) method to obtain the rotation matrix and introduces a novel weight estimation approach based on Gaussian function. This approach enables the weight to be normalized and optimized iteratively. To verify the accuracy and reliability of the proposed algorithm, the root mean square errors (RMSEs) from both true and pseudo-observed values are analyzed by simulation experiments. Furthermore, the results of simulated and empirical experiments show that the proposed algorithm can effectively reduce the influence of gross errors to obtain reliable measurement datum transformation parameters. It should be noted that the new algorithm can easily be extended to the 2D/3D affine and rigid transformation cases, such as image matching, point cloud registration, and absolute orientation of photogrammetry.
To ensure that the crane can smoothly calibrate and align the lifting rod with the beam body lifting hole, it is necessary to use image processing technology to locate and detect the corner coordinates of the crane’s lifting rod. Traditional corner detection methods are not suitable for this scene. This article proposes a new idea for corner positioning, which locates corner coordinates through the intersection of straight lines. Firstly, using the R and G channels of the RGB color space to construct a grayscale difference map is beneficial for Otsu’s threshold segmentation; Secondly, this article proposes an optimal adaptive threshold determination method to filter the number of votes in the clustering results, eliminate interfering straight lines, and improve the clustering centroid calculation method based on the weight calculation formula of different voting proportion, replacing the original clustering centroid as the basis for line fitting; Finally, calculate the corner coordinates of the crane’s grab boom based on the straight line fitting results, and compare the recognition accuracy under different lighting conditions. This method is significantly superior to traditional corner detection methods, providing a method basis for solving the algorithm accuracy and robustness problems of port cranes under multiple environmental variables.
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