基于多向联合交汇的柔性视觉形貌测量新方法

景江; 杨凌辉; 林嘉睿; 郭寅

doi:doi:10.5768/jao201738.0303002

应用光学, 2017, 38 (3): 438, 网络出版: 2017-06-30

基于多向联合交汇的柔性视觉形貌测量新方法

Flexible vision shape measurement by multi-way combination intersection

论文大纲

景江杨凌辉林嘉睿郭寅

作者单位

天津大学精密测试技术及仪器国家重点实验室, 天津 300072

形貌测量视觉测量位姿测量 PnP问题光束平差 shape measurement vision measurement pose estimation PnP problem ray bundle adjustment

摘要

针对大尺寸物体形貌测量应用, 设计一种视觉形貌测量新方法。该方法是将两套没有公共视场的单目视觉传感器组合起来联合交汇, 分别用于位姿测量和单目多位置交汇测量。位姿测量通过求解PnP问题为单目多位置交汇测量提供位姿信息。单目多位置交汇测量利用位姿测量提供的辅助信息通过光束平差解算出待测物的形貌。分析了系统原理, 给出位姿测量PnP算法实现过程, 介绍了非公共视场相机的标定方法原理, 分析了单目多位置交汇测量的原理并给出实现方法。最后进行了测量方法的验证实验, 实验结果表明, 在3 m×3 m的工作范围内系统测量精度为5 mm, 证明该方法可以用于大尺寸物体形貌测量, 且结构简单轻便, 具有很强的灵活性。

Abstract

This paper describes new vision measurement method for shape measurement of large-size object. Combine and intersect two monocular vision measurement systems without public filed of view for measurements of pose estimation and monocular vision intersection respectively. Pose estimation system solves the PnP problem to provide position information for monocular measurement. Monocular measurement system combining with auxiliary information from pose estimation calculates shape information of object. This paper analyses the system principle, gives PnP algorithm’s implementation process of pose estimation system, and introduces the principle and implementation process of calibration method for camera without public field of view. Finally, static system verification test of measurement system of indoor large-size object has been carried out, and the feasibilities of this system have been validated. Experimental results show that measuring accuracy of system is 5 mm in the scope of the 3 m×3 m. It is proved that this system can be used in shape measurement of large-size object, and the structure of system is simple and light with great flexibility.

参考文献

[1] Rosin P L. Shape-measuring the orientability of shapes[J]. Lecture Notes in Computer Science, 2007,4673: 620-627.

[2] 吴斌, 大型物体三维形貌数字化测量关键技术研究[D]. 天津: 天津大学, 2002.

Wu Bin, Study on the key technologies of 3D digital measurement of large-scale objects [D]. Tianjin: Tianjin University, 2002.

[3] 黄桂平, 钦桂勤, 卢成静. 数字近景摄影大尺寸三坐标测量系统V-STARS的测试与应用[J]. 宇航计测技术,2009,02: 5-9+22.

Huang Guiping, Qin Guiqin, Lu Chengjing. Testing and application of the digital close-range photogrammetry for the large scale 3-D measurement V-STARS[J]. Journal of Astronsutic Metrology and Measurement, 2009, 02: 5-9+22.

[4] 北京天远三维科技有限公司, Digimetric, 三维摄影测量系统[EB/OL]. http: //www.3dscan.com.cn.

Beijing Tian Yuan three dimensional Technology Co, ltd. Digimetric. Three dimensional photogrammetry system[EB/OL]. Beijing: Tian Yuan three dimen sional technology co, 1td, 2016[2016-08-08]. http: //www.3dscan.com.cn.

[5] 梁晋,肖振中,臧顺来,等.外差式多频相移技术的三维光学点云测量研究[J]. 锻压技术, 2008,33(1): 143-147.

Liang Jin, Xiao Zhenzhong, Zang Shunlai, et al. Study on 3D optical points dense cloud measuring system based on heterodyne multiple frequency phase shift technology[J].Forging & Stamping Technology, 2008,33(1): 143-147.

[6] 任同群.大型3D形貌测量高精度拼接方法与技术研究[D].天津: 天津大学,2008.

Ren Tongqun. The study on high-accuracy mosaic for large 3D free-form measurement[D].Tianjin: Tianjin University, 2008.

[7] Fischlerand M, Bolles R. Random sample consensus: a paradigm for model fittingwith applications to image analysis and automated cartography[J]. Communicationsof the ACM, 1981, 24: 381-385.

[8] 夏军营. 空间目标的单目视觉位姿测量方法研究[D].长沙: 国防科学技术大学, 2012.

Xia Junying. Researches on monocular vision based pose measurements for space targets[D].Changsha: Graduate School of National University of Defense Technology,2012.

[9] 吴福朝,胡占义.PnP问题的线性求解算法[J]. 软件学报, 2003,14(3): 682-688.

Wu Fuchao, Hu Zhanyi. A linear method for the PnP problem.[J] Journal of Software, 2003,14(3): 682-688.

[10] Zheng Y, Kuang Y, Sugimoto S, et al. Revisiting the PnP problem: a fast, general and optimal solution [C]. sydney: Computer Vision(ICCV), 2013 IEEE International Conference on, 2013: 2344-2351.

[11] 李鑫, 龙古灿, 刘进博, 等.相机位姿估计的加速正交迭代算法[J]. 光学学报, 2015, 35(1): 0115004.

Li Xin, Long Gucan, Liu Jinbo, et al. Accelerative orthogonal iteration algorithm for camera pose estimation[J]. Acta Optica Sinica, 2015,35(1): 0115004.

[12] Lowe D G. Fitting parameterized three-dimensional modelstoImages [J]. IEEE Trans-actions on Pattern Analysis and Machine Intelligence, 1991, 13(5): 441-450.

[13] 郭磊. 移动视觉精密测量关键技术研究[D].天津: 天津大学, 2011.

Guo Lei. Study on key techniques of precision portable vision metrology[D].Tianjin: Tianjin University, 2011.

[14] 邾继贵,于之靖.视觉测量原理与方法[M].北京: 机械工业出版社, 2012: 10-25.

Zhu Jigui, Yu Zhijing. The principle of vision metrology[M]. Beijing: China Machine Press, 2012: 10-25.

[15] 樊巧云,张广军.离散噪声图像的光斑质心算法及其硬件实现[J].光学精密工程,2011,19(12): 2992-2998.

Fan Qiaoyun, Zhang Guangjun. Spot centroiding algorithm for discrete noise image and its hardware implementation[J]. Optics andPrecision Engineering, 2012, 19(12): 2992-2998.

景江, 杨凌辉, 林嘉睿, 郭寅. 基于多向联合交汇的柔性视觉形貌测量新方法[J]. 应用光学, 2017, 38(3): 438. Jing Jiang, Yang Linghui, Lin Jiarui, Guo Yin. Flexible vision shape measurement by multi-way combination intersection[J]. Journal of Applied Optics, 2017, 38(3): 438.

基于多向联合交汇的柔性视觉形貌测量新方法

关于本站 Cookie 的使用提示

全站搜索

基于多向联合交汇的柔性视觉形貌测量新方法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索