Real-time deformation monitoring method of large structural parts based on transfer camera

Abstract

The invention discloses a real-time deformation monitoring method for a large structural member based on a transmission camera. The method comprises the following steps: 1, establishing a three-dimensional rectangular coordinate system; 2, acquiring external parameters of the transmission camera; 3, obtaining external parameters of a measurement camera; 4, acquiring internal parameters of the measurement camera; 5, measuring non-mark points; and 6, repeating the steps 2 to 5 for many times until real-time deformation measurement work of the large structural member is completed for one cycle. The method is based on the transmission camera which is fixedly connected with the measurement camera, a real-time pose relationship between the camera and a space point is transmitted, real-time external parameters of the measurement camera in a mobile measurement process are solved, the internal parameters of the measurement camera are solved in real time after that pose parameters of the camerabetween the adjacent moments are obtained, real-time solving of the parameters in the measurement camera is completed, real-time monitoring of structural part deformation is further completed, the environmental disturbance influence on the camera in the moving measurement process is well overcome, and deformation of the area is judged through coordinate position changes in different moments.

Description

technical field [0001] The invention belongs to the technical field of deformation monitoring, and in particular relates to a method for real-time deformation monitoring of large structural parts based on a transfer camera. Background technique [0002] In the process of damage monitoring of large structural parts, in order to effectively improve the efficiency and accuracy of damage monitoring, mobile vision methods are often used to monitor the deformation of structural parts in real time. In recent years, the research on damage monitoring of large structural parts using mobile vision methods However, in the process of real-time monitoring of structural parts, most of the research methods have shortcomings such as large error in solving external parameters of the camera, no consideration of changes in internal parameters, low real-time performance, and low degree of automation; In the research work of mobile vision measurement, in order to achieve the purpose of global mob...

AI Technical Summary

Problems solved by technology

[0002] In the process of damage monitoring of large structural parts, in order to effectively improve the efficiency and accuracy of damage monitoring, mobile vision methods are often used to monitor the deformation of structural parts in real time. In recent years, the research on damage monitoring of large structural parts using mobile vision methods However, in the process of real-time monitoring of structural parts, most of the research methods have shortcomings such as large error in solving external parameters of the camera, no consideration of changes in internal parameters, low real-time performance, and low degree of automation; In the research work of mobile vision measurement, in order to achieve the purpose of global mobile measurement, most methods need to complete the calibration measurement of the internal parameters of the camera in advance, and assume that the internal parameters are fixed, and then perform calibration measurement on the real-time changing external parameters, and then in On the basis of obtaining the internal and external parameters of the camera, complete the relevant measurement work according to the principle of camera measurement

Although these methods can obtain high-precision external parameters of camera pose transformation to a certain extent, these methods either require high precision of the controllable platform, and the cost is too high, or the operation is cumbersome and the degree of automation is low. During the movement of the camera, it is constantly disturbed by factors such as environmental vibrations, the accuracy of the solution of the external parameters will be affected by the disturbance, and its internal parameters will also change to a certain extent. Therefore, in the actual mobile measurement with high requirements for measurement accuracy In the work, in addition to the solution error of the external parameters, the above methods will further cause greater measurement errors because they ignore the changes of the internal parameters; considering the changes of the internal parameters during the measurement process, the existing methods use the self-calibration method to Solve by changing the internal parameters, or complete the optimization of the internal parameters through the constraint relationship of the known identification points in the scene, but for mobile measurement work, the process of optimizing the solution is more complicated, and the real-time performance of the solution is not strong

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