零件的角点提取及匹配定位 -

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零件的角点提取及匹配定位

doi:10.3788/CO.20160904.0397

天津大学精密测试技术及仪器国家重点实验室, 天津 300072

基金项目:

天津市自然科学基金重点资助项目No.13JCZDJC34500

详细信息

作者简介:
高礼圳：髙礼圳(1991-),男,江西吉安人,硕士研究生,主要从事图像处理和计算机视觉方面的研究。E-mail:gaolz1002@163.com

通讯作者:
刘书桂(1954-),男,湖南人,教授,博士,博士生导师,主要从事智能坐标测量技术、自动测量与控制技术方面的研究。E-mail:sgliu@tju.edu.cn

中图分类号:TH744.3
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出版历程
- 收稿日期:2016-04-05
- 修回日期:2016-04-26
- 刊出日期:2016-08-01

Corner extraction and matching location of parts

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China

Funds:

Key Project of Natural Science Foundation of Tianjin CityNo.13JCZDJC34500

More Information

Corresponding author:E-mail:sgliu@tju.edu.cn

摘要

摘要:本文基于三坐标测量机（CMM）设计了一套视觉检测系统，该系统能够对零件实际空间特征信息进行比较全面地提取。针对位于CMM平台上带有角点的零件，利用Harris算子提取从CMM三个不同方位获取的零件图像的角点。对于Harris算子提取到的角点，本文提出一种八链码搜索法和SUSAN区域法相结合的伪角点剔除方法，最后基于立体视觉的原理，提出“距离空间图”匹配算法，将以上3幅图像一一建立匹配关系。实验中多次改变零件在CMM中姿态时，多次实验数据表明本文的角点提取精度与真实角点间仅存在1~2像素的偏差，零件的定位误差为1~3mm。通过实验验证，角点匹配和定位的稳定性和精度满足要求，具有一定的抗干扰性和实用性。
- 三坐标测量机/
- Harris算子/
- 角点匹配/
- 立体视觉/
- 距离空间图
Abstract:In this paper, a set of visual inspection system is designed based on three coordinate measuring machine(CMM), and the new visual system can extract the actual spatial feature information of the parts. For the parts with corner which is on the platform of CMM, the Harris operator is used to extract corners in the images obtained from three different orientation of the CMM. For the corner points extracted by the Harris operator, this paper proposes a method of eliminating the false corners, which combines eight-chain-code false corner search method and SUSAN method. Finally, based on the principle of stereo vision, the "Distance spatial map" matching algorithm is put forward, and then the three images are matched one by one. Although the position and orientation of parts are changed for many times in the experiment, the experiment results show that there are 1-2 pixels difference between extracted corners and the real corner points, and the position error of parts is 1-3 mm. Through the experiment, the accuracy and stability of corner matching and positioning can meet the requirements, and with anti-interference and practicability as well as.
- CMM/
- Harris operator/
- corner matching/
- stereo vision/
- distance spatial map

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图 1八链码的方向图

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Figure 1.Eight-chain-code direction

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图 2相机与CMM的方位图

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Figure 2.Orientation diagram of camera and CMM

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图 3对极几何约束图

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Figure 3.Diagram of epipolar geometry

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图 4多视图确定空间点

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Figure 4.Multiple views to determine the point

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图 5距离空间图

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Figure 5.Diagram of distance space

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图 6角点图像

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Figure 6.Corner detection image

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图 7特征匹配结果

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Figure 7.Matching result

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表 1相机内部参数

Table 1.Camera internal parameter

	μ₀/mm	v₀/mm	f/mm	k₂
Camera1	658.884	533.363	16.193 040	0.000 289
Camera2	647.305	498.355	6.042 961	0.006 233
Camera3	647.598	498.715	16.235 531	0.000 288

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表 2角点在各视图中的像素坐标

Table 2.Pixel coordinates of corners in each view image

	Camera1(像素)	Camera2(像素)	Camera3(像素)
1	(544,957)	-	(758,840)
2	(533,805)	(763,591)	-
3	-	(783,560)	(791,690)
4	(815,723)	(546,310)	(526,631)

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表 3相机与CMM之间的坐标转换关系

Table 3.Relation of coordinate transformation between camera and CMM

	α/rad	β/rad	γ/rad	t_x/mm	t_y/mm	t_z/mm
Camera1-CMM	-1.503 283	0.064 378	-0.046 754	227.974 026	-1 050.507 167	-400.084 751
Camera2-CMM	0.044 251	-0.248 223	-1.523 188	308.677 97	870.639 171	30.065 223
Camera3-CMM	-1.543 118	-0.080 176	-1.524 048	-1529.503 317	624.449 218	-522.936 725

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表 4定位精度检测

Table 4.Positioning accuracy detection

	计算坐标/mm			CMM测量坐标/mm			误差/mm
	x	y	z	x′	y′	z′	Δx	Δy	Δz
1	229.98	607.47	-752.80	228.26	608.13	-753.52	1.72	-0.79	0.72
2	229.13	607.06	-667.91	229.84	608.94	-668.76	-0.71	-1.88	0.85
3	232.87	602.95	-667.16	233.64	604.06	-668.52	-0.77	-1.11	1.36
4	402.91	777.03	-658.04	401.29	778.21	-658.92	1.62	-1.18	0.88

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