Balancing optimization algorithm for target measuring head

Abstract

The invention discloses a balancing optimization algorithm for a target measuring head. The invariant relation of a vector in the Euclidean space and the photographic space is found to be used as a constraint condition, space vectors from handheld target surface feature points B to the target measuring head A under different poses and imaging rules under a pinhole camera model are built, the constraint condition is used as a condition equation to build a function model, Helmet posterior variance estimation is adopted as a random model, finally, the difference value is brought into the condition equation, and difference value testing is completed. According to the algorithm, the space position information of each handheld target surface feature point is sufficiently utilized, the robustness is good, the stability is high, 3D space reduction precision of the handheld target measuring head is improved, and the effective method is provided for further improving the binocular stereoscopic vision measuring precision.

Description

【Technical field】 [0001] The invention belongs to the technical field of visual measurement, and is an adjustment optimization algorithm that can improve the measurement accuracy of a binocular stereo vision system and is used to restore the spatial 3D coordinates of a hand-held target measuring head. 【Background technique】 [0002] With the increasing demand for the detection of the inner cavity shape of various special-shaped workpieces, the portable 3D coordinate measurement technology based on stereo vision measurement has been more and more applied. The binocular stereo vision system only needs two cameras, a calibration board, a hand-held target and a special probe to complete the in-situ / on-line measurement of large and complex cavity parts. It is not affected by the site environment and has high reliability, especially for It has unique advantages in the measurement of complex inner cavities and deep long holes. [0003] In a binocular stereo vision system, a hand-h...

AI Technical Summary

Problems solved by technology

After obtaining the distance from each LED feature point on the target surface to the probe, only three non-collinear feature points can be used to establish the target coordinate system. If N LED feature points are arranged on the target surface, the total can be Establish A target coordinate system (assuming that there are no any collinear 3 LED feature points), using the target coordinate system as the medium of coordinate transmission can be obtained The spatial 3D coordinates of the points to be measured, and the average sum of these 3D coordinates can obtain the final spatial 3D coordinates of the points to be measured. Although the average value can be used to reduce the error, the variance of these data is relatively large

[0008] Finally, since the mean value of the spatial 3D coordinates of the points to be measured is used, there is no difference in the weight of each data in the final result, and the reliability of each data cannot be effectively measured, so it is possible to amplify the error

[0009] The above problems are being paid more and more attention by domestic and foreign scholars. At present, there are few domestic and foreign related literatures on such problems, and there is no complete and relatively mature solution.

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