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We propose a robust and accurate camera pose determination method based on geometric optimization search using the Internet of Things (IoT). The central idea is to (1) obtain image information through Internet of Things technology, (2) obtain the first pose by minimizing the error function, and (3) use the geometric relationship and constraint condition to obtain the appropriate attitude angles as a new initial value for the next iteration calculation. The features of this method are as follows. First, this method can deal with a large amount of uncertain data, such as in the case of any shooting angle, in the case of any reference point, and in the case of a small number of feature points. Finally, because of using Internet of Things technology, our method can quickly complete data processing and transmission. Compared to state-of-the-art methods, the experimental results show that our approach performs well on both synthetic and real data and can be used to provide accurate and stable data for subsequent applications.

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.