基于系统空间结构约束的多线结构光条纹中心线提取方法

doi:10.16180/j.cnki.issn1007-7820.2022.03.003

摘要/Abstract

摘要：

线结构光测量系统容易受到环境光干扰从而影响条纹中心线的提取。通过对系统空间结构约束的分析,文中提出一种多线结构光条纹中心线的提取方法。该方法分为标定和测量两个阶段,在标定阶段求出中心线的偏移量和偏移系数;在测量阶段中,分别在有环境光和无环境光时求得条纹中心线的三维世界坐标。通过平面和曲面拟合实验测量精度,并与Steger算法进行对比。实验结果表明,当存在环境光时,斜平面拟合得到的误差值小于0.007 mm,曲面拟合得到的百分比相对误差小于1.2%;无环境光时,圆柱体高度拟合相对误差小于0.7%,说明本文算法精度优于Steger算法。

关键词: 线结构光, 环境光, 条纹中心线, 系统空间结构约束, 多线结构光条纹, 偏移量, 偏移系数, 平面拟合, 曲面拟合

Abstract:

The linear structured light measurement system is easy to be disturbed by ambient light, which affects the extraction of the center line of the fringe. Based on the analysis of the spatial structure constraints of the system, this study presents a method to extract the center line of multi-line structural light stripes. The extraction method is divided into two stages: calibration and measurement. In the calibration phase, the offset and offset coefficient of the center line are calculated. In the measurement stage, the three-dimensional world coordinates of the stripe center line are obtained with and without ambient light, respectively. The experimental accuracy is measured by plane and surface fitting and compared with Steger algorithm. The experimental results show that in the presence of ambient light, the error of skew plane fitting is less than 0.007 mm, and the percentage relative error of surface fitting is less than 1.2%. In the absence of ambient light, the relative error of cylinder height fitting is less than 0.7%. The above results indicate that the accuracy of the proposed algorithm is better than that of the Steger algorithm.

Key words: linear structured light, the ambient light, stripe center line, system space structure constraints, multi-line structured light stripe, the offset, the offset coefficient, plane fitting, surface fitting

中图分类号:

TN249

毛雪忆,李文国. 基于系统空间结构约束的多线结构光条纹中心线提取方法[J]. 电子科技, 2022, 35(3): 16-24.

Xueyi MAO,Wenguo LI. Multi-Line Structured Light Centerline Extraction Method Based on System Space Structure Constraints[J]. Electronic Science and Technology, 2022, 35(3): 16-24.

图/表 24

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

表1

表2

图13

图14

图15

表3

图16

表4

图17

图18

图19

表5

参考文献 19

[1]	Tran T T, Ha C. Non-contact gap and flush measurement using monocular structured multi-line light vision for vehicle assembly[J]. International Journal of Control Automation and Systems, 2018,16(5):2432-2445.
[2]	宋广瀛, 汪鑫, 滕飞, 等. 计算机三维数字化测量与手工测量PAR指数的一致性分析[J]. 中华口腔正畸学杂志, 2019,26(1):22-26.
	Song Guangying, Wang Xin, Teng Fei, et al. Consistency of peer assessment rating index by computer 3D digital measurement and manual measurement[J]. Chinese Journal of Orthodontics, 2019,26(1):22-26.
[3]	Herraez J, Martinez J C, Coll E, et al. 3D modeling by means of video grammetry and laser scanners for reverse engineering[J].Measurement, 2016(87):216-227.
[4]	张力, 黄影平. 实时双目立体视觉系统的实现[J]. 电子科技, 2016,29(3):68-70.
	Zhang Li, Huang Yingping. Implementation of real-time binocular stereo vision system[J]. Electronic Science and Technology, 2016,29(3):68-70.
[5]	Wang H F, Wang Y F, Zhang J J, et al. Laser stripe center detection under the condition of uneven scattering metal surface for geometric measurement[J]. IEEE Transactions on Instrumentation and Measurement, 2020,69(5):2182-2192.
[6]	Peters F, Mohlhenrich S C, Ayoub N, et al. The use of mobile 3D scanners in maxillofacial surgery[J]. International Journal of Computerized Dentistry, 2016,19(3):217-230.
[7]	徐德明, 万长林. 可抗噪声的Canny改进边缘检测算法[J]. 计算机系统应用, 2017,26(1):201-205.
	Xu Deming, Wan Changlin. Noise-proof enhanced canny edge detection algorithm[J]. Computer Systems & Applications, 2017,26(1):201-205.
[8]	南方, 李大华, 高强, 等. 改进Steger算法的自适应光条纹中心提取[J]. 激光杂志, 2018,39(1):85-88.
	Nan Fang, Li Dahua, Gao Qiang. Implementation of adaptive light stripe center extraction of improved Steger algorithm[J]. Laser Journal, 2018,39(1):85-88.
[9]	黄琬婷, 胡小平. 一种基于张氏标定法的单目相机改进标定算法[J]. 导航与控制, 2019,18(1):105-111.
	Huang Wanting, Hu Xiaoping. An improved calibration algorithm of monocular camera based on Zhang's plane calibration method[J]. Navigation and Control, 2019,18(1):105-111.
[10]	Zou Y, Niu L H, Su B H, et al. Calibration of 3D imaging system based on multi-line structured-light[C]. Beijing:Three-Dimensional Image Acquisition and Display Technology and Applications, 2018.
[11]	史伟忠, 曹卫卫, 范彦铭, 等. 基于FPGA的深空图像实时边缘检测算法与实现[J]. 电子科技, 2020,33(5):45-49.
	Shi Weizhong, Cao Weiwei, Fan Yanming, et al. FPGA-based real-time edge detection and its implementation for deep-space images[J]. Electronic Science and Technology, 2020,33(5):45-49.
[12]	李长有, 陈国玺, 丁云晋. 改进Canny算子的边缘检测算法[J]. 小型微型计算机系统, 2020,41(8):1758-1762.
	Li Changyou, Chen Guoxi, Ding Yunjin. Improved edge detection algorithm for canny operator[J]. Journal of Chinese Computer Systems, 2020,41(8):1758-1762.
[13]	Li W G, Li H, Zhang H. Light plane calibration and accuracy analysis for multi-line structured light vision measurement system[J]. Optik, 2020,20(7):1-11.
[14]	杨琼楠, 马天力, 杨聪锟, 等. 基于优化采样的RANSAC图像匹配算法[J]. 激光与光电子学进展, 2020,57(10):259-266.
	Yang Qiongnan, Ma Tianli, Yang Congkun, et al. RANSAC image matching algorithm based on optimized sampling[J]. Laser & Optoelectronics Progress, 2020,57(10):259-266.
[15]	占栋, 肖建. 基于线结构光参考平面的多摄像机灵活标定方法研究[J]. 仪器仪表学报, 2015,36(9):2030-2036.
	Zhan Dong, Xiao Jian. Study on the flexible multiple camera calibration approach based on the reference plane emitted by line structured light[J]. Chinese Journal of Scientific Instrument, 2015,36(9):2030-2036.
[16]	杨以铁, 郭天太, 孔明. 多线结构光堆积物三维测量方法研究[J]. 中国测试, 2019,45(11):35-39.
	Yang Yitie, Guo Tiantai, Kong Ming. Three-dimensional measurement method of deposit based on multi-line structured light[J]. China Measurement & Test, 2019,45(11):35-39.
[17]	Li B W, Zhang S. Structured light system calibration method with optimal fringe angle[J]. Applied Optics, 2014,53(33):7942-7950.
[18]	康传利, 时满星, 程耀, 等. 移动最小二乘重采样在三维重建中的应用[J]. 桂林理工大学学报, 2019,39(3):650-655.
	Kang Chuanli, Shi Manxing, Cheng Yao, et al. Application of mobile least squares resampling in 3D reconstruction[J]. Journal of Guilin University of Technology, 2019,39(3):650-655.
[19]	石元博, 卢洋, 黄越洋. 基于加权最小二乘法对图像增强方法的研究[J]. 电子设计工程, 2019,27(14):172-175.
	Shi Yuanbo, Lu Yang, Huang Yueyang. Research on image enhancement methad based on weighted least square method[J]. Electronic Design Engineering, 2019,27(14):172-175.

实验组别	无环境光时 Steger算法误差/mm	无环境光时本文方法的误差/mm	有环境光时 Steger算法误差/mm	有环境光时本文方法的误差/mm
1	0.004 8	0.005 0	-	0.006 2
2	0.004 0	0.004 1	-	0.005 8
3	0.004 6	0.004 9	-	0.005 0
4	0.004 0	0.004 3	-	0.005 3
5	0.004 5	0.004 6	-	0.006 6
6	0.005 0	0.004 9	-	0.006 7

实验组别	圆柱标准高 /mm	Steger法拟合值 /mm	Steger法相对误差 /mm	本文方法拟合值 /mm	本文方法相对误差 /%
1	70	70.50	0.71	70.51	0.73
2	70	70.58	0.83	70.44	0.63
3	70	70.45	0.64	70.47	0.67
4	70	70.55	0.79	70.52	0.74
5	70	70.60	0.86	70.45	0.64
6	70	70.43	0.61	70.49	0.70

实验组别	半球直径 /mm	Steger法拟合值/mm	本文方法拟合值/mm	本文方法相对误差 /%
1	50	-	50.65	1.30
2	50	-	50.58	1.16
3	50	-	50.55	1.10
4	50	-	50.61	1.22
5	50	-	50.51	1.02
6	50	-	50.52	1.04