基于线结构光的复合型坡口特征点提取算法

    Feature Point Extraction Algorithm of Compound Groove Based on Linear Structured Light

    • 摘要: 在使用结构光视觉系统提取复合型坡口特征点时,由于坡口表面反射和环境光干扰等导致特征点提取困难,提出了一种基于线结构光的复合型坡口特征点提取算法以适应不同噪声下的环境。首先,对结构光视觉传感器获取的线激光条纹图像进行预处理,采用自适应阈值分割以减小不同亮度和对比度的影响,利用骨架提取获得焊缝的中心点。其次,通过积分法和最小二乘法构造高斯密度函数对焊缝中心线进行拟合。最后,根据修正的列文伯格-马夸尔特算法(Levenberg-Marquardt,L-M)对函数进行调整,对数据点和模型预测之间的残差进行了最小化。结果表明,所提焊缝特征点的均方根误差小于0.20 mm,满足在实际焊接过程中的要求。

       

      Abstract: When using structured light vision system to extract composite groove feature points, it is difficult to extract feature points due to the surface reflection of the groove and the interference of ambient light. A novel algorithm based on linear structured light was proposed to adapt to the environment under different noises. Firstly, the linear laser fringe image obtained by the structured light vision sensor was preprocessed, adaptive threshold segmentation was used to reduce the influence of different brightness and contrast, and the center point of the weld was obtained by skeleton extraction. Secondly, Gaussian density function was constructed to fit the weld center line by integral method and least square method. Finally, the function was adjusted according to the modified L-M algorithm, and the residual difference between the logarithmic data point and the model prediction was minimized. The results show that the root-mean-square error of the proposed method is less than 0.20 mm, which can meet the requirements in the actual welding process.

       

    /

    返回文章
    返回