融合图像与点云的曲面工件涂胶质量检测方法

A method of gluing quality detection for curved workpieces based onimage and point clouds fusion processing

提出了一种融合图像与点云的曲面工件涂胶质量检测方法,解决了基于图像的涂胶质量检测精度低、鲁棒性差的问题。方法包括利用圆形Mark点进行粗定位,引入法矢一致性约束的改进迭代最近点算法完成精定位,并提取图像中的胶线骨架信息转化为3D胶线轨迹点。通过等距有序采样方法获取采样点进行胶线质量参数检测。根据采样点的法向约束和胶线轨迹切向约束,获得采样点胶线截面模型,将点云映射到截面中得到胶线截面轮廓模型,进一步分析截面轮廓以计算胶线质量参数。实验证明,测量得到的胶线宽度误差小于0.35 mm,厚度误差小于0.25 mm,满足工业场景中的胶线质量评估要求。

In this paper,a method of gluing quality detection for curved workpiece based on image and point cloud is proposed,which solves the problem of low accuracy and poor robustness of image-based gluing quality detection.The method includes using the circular Mark point for rough positioning,introducing the improved iterative closest point algorithm with normal vector consistency constraint to complete the fine positioning,and extracting the glue skeleton information from the image into 3D glue trace points.The sampling points were obtained by equidistant and ordered sampling method to detect the quality parameters of the glue line.According to the normal constraint of sampling point and tangential constraint of glue trace,the sampling glue trace cross section model was obtained,and the point cloud was mapped to the cross section to get the glue trace cross section profile model.Experimental results have shown that the measured width error of the glue line is less than 0.35mm,and the thickness error is less than 0.25mm,which meets the quality evaluation requirements of glue lines in industrial scenarios.