首页|基于改进GWO-GRNN的管道焊缝三维重构测量

基于改进GWO-GRNN的管道焊缝三维重构测量

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为提高双目相机不同位姿下焊缝的三维重构测量精度，提出一种基于立体视觉图像误差补偿的管道焊缝三维重构测量方法。采用改进灰狼算法(IGWO)优化广义回归神经网络(GRNN)补偿焊缝三维重构图像点的坐标误差。采用混沌映射、非线性收敛因子和最优记忆保存思想对GWO算法进行改进，通过8个标准测试函数进行仿真验证;利用优化后的GRNN模型对图像点坐标误差进行预测和补偿，计算三维坐标重构出焊缝点云，三维测量焊缝的焊宽、余高和长度。试验结果表明:该模型在双目相机不同的位姿状态下都能较准确地实现焊缝的三维重构，焊缝的三维测量相对误差在0。9％以内。
Three-Dimensional Reconstruction Measurement of Pipeline Weld Based on Improved GWO-GRNN
In order to improve the measurement accuracy of 3D reconstruction of welds under different poses of binocular cameras,a 3D reconstruction measurement method of pipeline welds based on stereo vision image error compensation was proposed.The improved gray wolf algorithm(IGWO)was used to optimize the generalized regression neural network(GRNN)to compensate the coordinate er-ror of the 3D reconstructed image points of the weld.The GWO algorithm was improved by chaotic mapping,nonlinear convergence factor and optimal memory preservation idea,and the simulation verification was carried out through 8 standard test functions.The IGWO was used to construct the point cloud of the weld,and the weld width,height and length were measured in three dimensions.The experimental results show that the model can accurately achieve the 3D reconstruction of the weld seam under different poses of the binocular camera.The relative error of the 3D measurement of the weld seam is within 0.9％.

stereo visionimage error compensationimproved gray wolf algorithm(IGWO)generalized regression neural network(GRNN)3D reconstruction measurement of pipeline weld

高博轩、赵弘、苗兴园

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中国石油大学(北京)机械与储运工程学院,北京 102249

立体视觉 图像误差补偿 改进灰狼优化 广义回归神经网络 焊缝三维重构测量

国家自然科学基金中国石油大学(北京)前瞻导向及培育项目

515755282462022QEDX011

2024

10.3969/j.issn.1001-3881.2024.01.001

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子：0.32
ISSN：1001-3881
年,卷(期)：2024.52(1)
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