一种大视场汇聚型双目立体视觉标定方法

doi:10.3969/j.issn.0255-8297.2024.02.008

Abstract

Abstract: The convergent placement of binocular cameras can lead to defocus blur and perspective deformation of the marked points, introducing positioning deviations and calibration errors, especially problematic in large field of view (FOV) environments and consequently affecting measurement accuracy. To address this problem, a large FOV convergence binocular stereo vision calibration method based on the weighting of mark points positioning deviation degree is proposed. Firstly, the defocus blur and perspective deformation of the mark points are calculated by using the position of the target in the camera coordinate system. Secondly, the corresponding weight is set according to the positioning deviation degree of mark points. Finally, the mark points weight coefficients are added to the objective function to guide the optimization of calibration parameters. Experimental results show that the root-mean-square error and standard deviation of distance measurement can reach 0.809 and 0.290, respectively, when the observation value is 505 mm. This method not only effectively improves the calibration accuracy of large field convergence binocular stereo vision, but also exhibits good stability.

Key words: stereo vision, convergent binocular calibration, large field of view (FOV), positional deviation, weighted optimization

CLC Number:

TP391.41

CUI Shuaihua, YU Lei, HE Xi, XIONG Bangshu, OU Qiaofeng. A Large FOV Convergence Binocular Stereo Vision Calibration Method[J]. Journal of Applied Sciences, 2024, 42(2): 269-279.

References

[1] 张宗华, 刘巍, 刘国栋, 等. 三维视觉测量技术及应用进展[J]. 中国图象图形学报, 2021, 26(6):1483-1502. Zhang Z H, Liu W, Liu G D, et al. Overview of the development and application of 3D vision measurement technology [J]. Journal of Image and Graphics, 2021, 26(6):1483-1502. (in Chinese)
[2] Shan F Q, Wang F, Zhang H F. Research on 3D pose measurement algorithm based on binocular vision [J]. Journal of Physics:Conference Series, 2021, 1792(1):012043.
[3] Wei B, Liang J, Hu H, et al. A videogrammetric system for measuring the full-field blade deformation of a heavy-duty helicopter's rotating rotor [J]. Review of Scientific Instruments, 2018, 89(11):115111.
[4] 熊邦书, 罗院华, 黄建萍, 等. 基于立体视觉的直升机桨叶扭转角模拟测量方法[J]. 应用科学学报, 2018, 36(6):950-957. Xiong B S, Luo Y H, Huang J P, et al. Measurement method of torsion of helicopter rotor blades based on stereo vision [J]. Journal of Applied Sciences, 2018, 36(6):950-957. (in Chinese)
[5] Wang S Z, Laskar Z, Melekhov I, et al. Continual learning for image-based camera localization [C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021:3232-3242.
[6] 周文雅, 李哲, 许勇, 等. 基于双目视觉的无人机编队相对定位算法[J]. 宇航学报, 2022, 43(1):122-130. Zhou W Y, Li Z, Xu Y, et al. Relative positioning algorithm of UAV formation based on binocular vision [J]. Journal of Astronautics, 2022, 43(1):122-130. (in Chinese)
[7] Yang L, Lei W M. Computer vision positioning and local obstacle avoidance optimization based on neural network algorithm [J]. Computational Intelligence and Neuroscience, 2022, 2022:3061910.
[8] Itu R, Borza D, Danescu R. Automatic extrinsic camera parameters calibration using convolutional neural networks [C]//201713th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 2017:273-278.
[9] 王伟, 张朝阳, 唐心瑶, 等. 道路场景下相机自动标定及优化算法[J]. 计算机辅助设计与图形学学报, 2019, 31(11):1955-1962. Wang W, Zhang C Y, Tang X Y, et al. Automatic self-calibration and optimization algorithm of traffic camera in road scene [J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(11):1955-1962. (in Chinese)
[10] Ci W Y, Xu T X, Lin R Z, et al. A novel method for unexpected obstacle detection in the traffic environment based on computer vision [J]. Applied Sciences, 2022, 12(18):8937.
[11] 张泽霞, 胡晓聪, 武楚晗, 等. 基于相机阵列的新型牙科三维扫描仪[J]. 光学技术, 2019, 45(1):112-116. Zhang Z X, Hu X C, Wu C H, et al. Novel dental 3D scanner based on camera array [J]. Optical Technique, 2019, 45(1):112-116. (in Chinese)
[12] 杨帆, 刘巍, 张洋, 等. 结合四角共线约束的大视场双目相机标定方法[J]. 光学学报, 2016, 36(7):0715001. Yang F, Liu W, Zhang Y, et al. Calibration method of binocular camera with large field of view combined with collinear constraint of four corners [J]. Acta Optica Sinica, 2016, 36(7):0715001. (in Chinese)
[13] 刘红帝, 吕睿, 田林雳, 等. 用于引导机器人定位的汇聚式双目视觉算法实现[J]. 机械工程学报, 2022, 58(14):161-169. Liu H, Lyu R, Tian L L, et al. Realization of convergent binocular vision algorithm for guiding robot localization [J]. Journal of Mechanical Engineering, 2022, 58(14):161-169. (in Chinese)
[14] Wang Z R, Chen G C, Tan L S. Optimization of stereo calibration parameters for the binocular camera based on improved Beetle Antennae Search algorithm [J]. Journal of Physics:Conference Series, 2021, 2029(1):012095.
[15] 张鹏炜, 蒋晓瑜, 吕家国, 等. 一种基于欧氏距离的双目视觉三维重建算法[J]. 激光与红外, 2013, 43(10):1166-1170. Zhang P W, Jiang X Y, Lyu J G, et al. 3D reconstruction algorithm based on Euclidean distance for binocular vision [J]. Laser & Infrared, 2013, 43(10):1166-1170. (in Chinese)
[16] Zhao C, Zhao Z R, Chen W Z. Low luminance measurement based on binocular ranging [C]//20185th International Conference on Systems and Informatics (ICSAI), 2019:983-987.
[17] 宋晓炜, 杨蕾, 吴源昭, 等. 基于双目会聚型立体摄像机的目标深度测量[J]. 激光与红外, 2015, 45(7):844-849. Song X W, Yang L, Wu Y Z, et al. Object depth measurement based on convergent binocular stereo camera [J]. Laser & Infrared, 2015, 45(7):844-849. (in Chinese)
[18] 闫龙, 赵正旭, 周以齐. 基于CCD的立体视觉测量系统精度分析与结构设计研究[J]. 仪器仪表学报, 2008, 29(2):410-413. Yan L, Zhao Z X, Zhou Y Q. Accuracy analysis and configuration design of stereo photogrammetry system based on CCD [J]. Chinese Journal of Scientific Instrument, 2008, 29(2):410-413.(in Chinese)
[19] 杨洪涛, 何海双, 李莉, 等. 双目立体视觉测量系统的精度分析[J]. 传感器与微系统, 2020, 39(10):58-61, 69. Yang H T, He H S, Li L, et al. Precision analysis of binocular stereo vision measurement system [J]. Transducer and Microsystem Technologies, 2020, 39(10):58-61, 69.(in Chinese)
[20] Jia Z, Li M, Liu W, et al. Layout optimization of binocular stereo vision measuring system [J]. Sensors & Transducers, 2013, 161(12):74.
[21] 陈荣刚, 刘国锋, 赵桃峰, 等. 基于会聚双目立体视觉系统的建模与仿真[J]. 仪器仪表学报, 2009:274-277. Chen R G, Liu G F, Zhao T F, et al. A model and simulation of the convergent stereo vision system [J]. Chinese Journal of Scientific Instrument, 2009:274-277.
[22] 彭传波. 基于激光编码的航空行李视觉检测方法研究[D]. 天津:中国民航大学, 2019.
[23] 姜学涛. 基于汇聚式双目视觉的轴类零件测量技术研究[D]. 杭州:浙江大学, 2019.
[24] 任飞, 李宏胜, 孙权, 等. 基于改进进化神经网络的双目视觉系统标定[J]. 电光与控制, 2021, 28(1):71-75. Ren F, Li H S, Sun Q, et al. Binocular vision system calibration based on improved evolutionary neural network [J]. Electronics Optics & Control, 2021, 28(1):71-75. (in Chinese)
[25] 王靖, 魏亮, 向文豪, 等. 考虑圆形特征边缘模糊和偏心误差修正的高精度相机标定方法[J]. 红外与激光工程, 2021, 50(12):369-379. Wang J, Wei L, Xiang W H, et al. High-precision camera calibration method considering projected circular edge blur and eccentricity error [J]. Infrared and Laser Engineering, 2021, 50(12):369-379. (in Chinese)
[26] Zhang Z. A flexible new technique for camera calibration [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.

A Large FOV Convergence Binocular Stereo Vision Calibration Method

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[2]	XIONG Bang-shu, LUO Yuan-hua, HUANG Jian-ping, LI Xin-min. Measurement Method of Torsion of Helicopter Rotor Blades Based on Stereo Vision [J]. Journal of Applied Sciences, 2018, 36(6): 950-957.
[3]	WANG Jing1,2, SU Guang-da1. Frontal Face Synthesis Based on Improved Binocular Stereo Vision [J]. Journal of Applied Sciences, 2014, 32(1): 74-78.
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