基于 2D 预处理的点云分割和测量研究

针对传统 3D 工业相机获取的点云数据进行工件检测时因工件粘连和噪声干扰导致边缘分割问题,考虑点云数据量大 影响检测实时性和 3D 特征点选取不准确导致测量误差大的因素,提出一种基于 2D 边缘检测的预处理方法,实现点云快速分 割和测量。 首先,采用改进的 Canny 算法对有序点云的纹理图像进行边缘检测,将检测后的图像进行数学形态学操作和轮廓检 测完成纹理图像分割,规避了在 3D 空间中进行分割处理,有效减少了点云数量;其次,结合工件的形状特征和放置方式,利用 掩膜操作提取出有序点云数据,使用基于 RANSAC 和条件滤波结合的方法对分割后的点云进行自适应阈值滤波处理,有效去 除了噪声点云;最后,对经过预处理后的目标点云基于 PCA 的包围盒去计算工件尺寸以及表面法向量。 实验结果表面,和传统 的 3D 分割算法相比,能够更准确的提取出目标点云,有效减少了待处理点云数量,整体分割效率提高了约 20%;工件尺寸的平 均相对误差约 1. 24%,可以满足测量的需求。

Research on point cloud segmentation and measurement based on 2D preprocessing

In view of the problem of edge segmentation caused by workpiece adhesion and noise interference when the point cloud data obtained by the traditional 3D industrial camera is used for workpiece detection, considering the factors that the large amount of point cloud data affects the real-time detection and the inaccurate selection of 3D feature points leads to large measurement error, a preprocessing method based on 2D edge detection is proposed to realize the rapid segmentation and measurement of point cloud. In the first place, the improved Canny algorithm is applied to detect the edge of the texture image of the ordered point cloud, and the detected image is separated by mathematical morphology operation and contours detection, which avoids the segmentation process in 3D space and effectively reduces the number of point clouds. In the second place, combined with the shape characteristics and placement mode of the workpiece, the ordered point cloud data was extracted by mask operation, and the adaptive threshold filtering was performed on the segmented point cloud based on the RANSAC and conditional filtering method to effectively remove the noise point cloud. Finally, the workpiece size and normal vector are calculated based on the bounding box of PCA for the preprocessed target point cloud. We could know from results that compared with the traditional 3D algorithm, it can extract the target point cloud more accurately, efficaciously decrease the amount of point cloud data, and improve the segmentation efficiency by about 20%. The average relative error of workpiece size is 1. 24%, which can meet the needs of measurement.