As a non-contact measurement technology with high data acquisition efficiency, photogrammetry is an ideal choice for collecting the data needed in the safety evaluation of port hoisting machinery. However, the radius fitting result accuracy cannot meet the requirements of safety assessment due to the limitation of the port crane itself and the working environment characteristics, when the existing photogrammetry method is used to measure the rotary body structure represented by the portal crane slewing mechanism. In order to solve this problem, an iterative optimization algorithm for weighted radius prediction for the photogrammetry of the slewing mechanism of port hoisting machinery is proposed in this paper. First, the algorithm uses the generalized multi-line rendezvous model to transform the radius fitting problem into the multi-line intersection point prediction problem, which lays a theoretical basis for the subsequent algorithm implementation. Second, by introducing a weighting algorithm based on the camera optical distortion model, the algorithm optimizes the accuracy of radius fitting results. In addition, through the quantitative evaluation method of fitting accuracy based on weighted algorithm, the algorithm also establishes a set of iterative rules to balance the accuracy of measurement results and the execution efficiency of the algorithm. Finally, this paper designs theoretical verification tests and simulation engineering tests based on the characteristics of the algorithm and the engineering practice of port hoisting machinery photogrammetry. The experimental results demonstrate that the algorithm described in this paper can significantly improve the accuracy of radius fitting results when the data quantity is small and the data quality is poor compared with the traditional algorithm.

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.

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