基于2D预处理的点云分割和测量研究  

Research on point cloud segmentation andmeasurement based on 2D preprocessing

作　　者：殷宗琨 江明[1,2] 柏受军 赵朝朝[1,2] Yin Zongkun;Jiang Ming;Bai Shoujun;Zhao Zhaozhao(Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment,Ministry of Education,Anhui Polytechnic University,Wuhu 241000,China;School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]安徽工程大学高端装备先进感知与智能控制教育部重点实验室 [2]安徽工程大学电气工程学院,芜湖241000

出　　处：《电子测量与仪器学报》2022年第9期53-63,共11页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61271377)项目资助。

摘　　要：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。In view of the problem of edge segmentation caused by workpiece adhesion and noise interference when the point cloud data obtained by the traditional 3 D 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 3 D feature points leads to large measurement error, a preprocessing method based on 2 D 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 3 D 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 3 D 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.

关 键 词：图像预处理 RANSAC拟合 包围盒提取 点云分割测量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] P237[自动化与计算机技术—计算机科学与技术]