SIA OpenIR  > 工艺装备与智能机器人研究室
基于视觉的汽车钣金件几何参数检测
Alternative TitleDimensional Inspection of Automobile Sheet Metal Parts Based on Computer Vision
苏润1,2
Department工艺装备与智能机器人研究室
Thesis Advisor夏仁波
Keyword钣金件检测 点云拼接 圆孔检测
Pages55页
Degree Discipline模式识别与智能系统
Degree Name硕士
2020-05-26
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract钣金件因为其质量轻、易被加工成结构复杂的零件等特点,在汽车工业中得到了大量使用。为了保证钣金件的加工质量和装配质量,必须对其结构的关键几何参数进行检测。目前,企业一般使用专用的工装夹具完成对钣金件的大批量检测。如果钣金件的型号被改变,原有的工装夹具将无法被重复使用,这造成了很大的浪费,给企业带来了巨大的经济负担。三坐标测量机可以适应对不同型号钣金件的检测,但由于其体积较大、检测速度较慢,只能运用于小批量的检测。因此,企业对实现钣金件的快速、高精度、高柔性的检测有着迫切需求。本文基于双目视觉技术,通过获取钣金件的表面点云和图像,对钣金件的几何参数检测进行了研究,主要研究内容如下:(1)获取完整的钣金件表面点云涉及到多视角点云的拼接,由于点云拼接是有误差的,多次拼接则会导致误差的累积。为了减少误差,本文提出了一种基于重投影误差最小化原理的双目稀疏光束平差法,实现了重建点云在多视角的双目图像下重投影误差最小化,减少了75%的拼接误差,提高了拼接精度。(2)圆孔是钣金件上一个重要的特征,经常被作为加工、装配的基准,因此必须被检测。在双目视觉系统中,重建孔的三维结构的难点在于从图像中准确寻找到孔的边缘。本文提出了一种高精度的圆孔检测方法。该方法通过寻找高度可信的孔边缘点完成单目图像中孔边缘的提取,并引入极线几何约束实现对双目图像中孔边缘的优化。该方法可使双目视觉系统在不同姿态下对不同直径、厚度的孔进行高精度检测,检测均方根误差小于0.05mm。(3)为了实现对钣金件的检测,本文开发了相应的硬件及软件系统,规范了检测流程。结合实验室开发的点云处理软件,实现了对钣金件上的孔、型面偏差、切边偏差等几何参数的检测,检测的平均误差小于0.05mm。
Other AbstractSheet metal parts are widely used in the automotive industry because of their light weight and can be processed into complex parts. In order to ensure the processing quality and assembly quality of sheet metal parts, their dimensional parameters must be inspected. Generally, enterprises use specific fixture equipment to inspect mass sheet metal parts. But once the model of the sheet metal part is changed, the original fixture equipment cannot be reused, which causes waste and puts economic burden to the enterprises. The coordinate measuring machine can be adapted to the detection of different models of sheet metal parts, but due to its large size and slow inspection speed, the coordinate measuring machine cannot be used to inspect mass parts. Therefore, enterprises have an urgent need to achieve the rapid, high-accuracy and high-flexibility inspection of sheet metal parts. This paper has studied the dimensional inspection of sheet metal parts based on computer vision and finished the inspection by analyzing the point clouds and images of the sheet metal parts. The main contents of the paper are as follows: (1) To obtain a complete point cloud of a sheet metal part, it involves point cloud stitching. Error exists in the stitching of two point clouds, and when more or larger point clouds are stitched, the error will be accumulated. In order to reduce the error, this paper proposes a method which is call binocular sparse bundle adjustment. By minimizing the reprojection errors in multi-view binocular images, the stitching error is reduced 75%. (2) Circular hole is an important feature on sheet metal parts because it is often used as benchmark in processing and assembly. In a binocular system, it is challenging to reconstruct the hole because it is difficult to accurately find the edge of the hole in images as well as the exact correspondence of the hole edge point extracted from the left and right images separately. This paper proposes a method for high-accuracy hole inspection. In this method, the edge of the hole in an image is extracted by finding highly trusted edge points, and the epipolar geometric constraint is introduced to find the hole edge in the binocular images. The method allows to inspect holes with various diameters and thicknesses from different views, and the root-mean-square error is less than 0.05mm. (3) In order to achieve the inspection of sheet metal parts, this paper develops a binocular system and a software, and the standard inspection process is also given. Using a point cloud processing software developed by our laboratory, the inspection of sheet metal parts’ holes, faces and sides is achieved. The average error of inspection is less than 0.05mm.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/27125
Collection工艺装备与智能机器人研究室
Affiliation1.中国科学院沈阳自动化研究所;
2.中国科学院大学
Recommended Citation
GB/T 7714
苏润. 基于视觉的汽车钣金件几何参数检测[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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