A weighting intersection point prediction iteration optimization algorithm used in photogrammetry for port hoisting machinery

A Weighting Radius Prediction Iteration Optimization Algorithm Used in Photogrammetry for Rotary Body Structure of Port Hoisting Machinery

“…The same idea as the weighting algorithm described in the paper "A weighting intersection point prediction iteration optimization algorithm used in photogrammetry for port hoisting machinery" [8] the measurement errors at each fitting point can be quantitatively evaluated based on the camera optical distortion model.…”

“…1, in the paper "A weighting intersection point prediction iteration optimization algorithm used in photogrammetry for port hoisting mechanism" published by the author before, point i is taken as an example to show that the key joint of port hoisting machinery structure belongs to the characteristics of multiline intersection point. d , 3 d from the predicted intersection point ' i (TPPOI) to each intersection line in the solution model (TEWIPPA), the quantified weighting of the observed data is realized based on the size of the theoretical optical distortion of each point while introducing redundant observation data, and a relatively ideal precision optimization effect is obtained [8] . However, this method does not apply to the safety evaluation research of the rotary body structure represented by the slewing mechanism of portal crane as shown in Fig.…”

“…3 and the perpendicular bisector ab l , bc l , cd l of them cannot intersect at the same point due to the existence of measurement errors. Therefore, according to the idea of establishing the predictive intersection point solution model (TEWIPPA) [8] , the center O can be obtained according to the principle shown in Fig. 5 in the actual measurement the principle shown in Fig.…”

“…As a non-contact measuring method with high data acquisition efficiency and excellent automation, photogrammetry is an ideal technical solution for port hoisting machinery safety monitoring. However, the application of photogrammetry in the field of port hoisting machinery safety monitoring has always been limited by the complex working environment of the port, which makes it impossible to get photos taken at reasonable distance and angle and with high background discrimination, resulting in unsatisfactory accuracy of measurement results calculated by traditional algorithm [5,6,7] .In order to solve this problem, the author and his team members put forward "a weighted intersection point prediction algorithm for photogrammetry of port hoisting machinery" based on the multi-line intersection points characteristics of key points of port hoisting machinery [8]. By weighting intersection lines based on optical distortion, the method realizes the principle of high-precision prediction of key points and significantly improves the measurement accuracy of key nodes of port hoisting machinery.…”

“…The algorithm uses the characteristic that the perpendicular bisector of each chord around the circle intersects at the center of the circle, and converts the roundness error caused by the measurement error of each point of the circle in the actual measurement process into the non-strict intersection of the mid-vertical line. This paper optimizes the circle center coordinates through the multi-line intersection point prediction model and the measurement accuracy of the radius obtained by the adjustment calculation based on the circle center coordinates [8] .On this basis, considering the different measurement errors caused by optical distortion at each point of the image plane, a weighted model for predicting and calculating the multi-line intersection point at the center of the circle is proposed, which realizes the quantitative description of the error introduced by optical distortion and further improves the measurement accuracy. Finally, the algorithm establishes a set of iteration rules based on the quantitative evaluation method of the fitting accuracy of weighted algorithm, which realizes the balance between the precision of measurement results and the execution efficiency of the algorithm.…”