基于归一化算法的一维标定物多相机标定  

Multi-Camera Calibration of One-Dimensional Calibration Objects Based on Normalization Algorithm

作　　者：全燕鸣[1] 覃镇波 李维诗[2] 张瑞[2] Quan Yanming;Qin Zhenbo;Li Weishi;Zhang Rui(School of Mechanical&A utomotive Engineering, South China University of Technology,Guangzhou, Guangdong 510640, China;Intelligent Measurement Team , Guangdong Academy, Hefei University of Technology,Foshan , Guangdong 528137, China)

机构地区：[1]华南理工大学机械与汽车工程学院,广东广州510640 [2]合肥工业大学广东研究院智能检测团队,广东佛山528137

出　　处：《光学学报》2019年第4期247-254,共8页Acta Optica Sinica

基　　金：佛山市科技创新团队项目(2014IT100115)

摘　　要：为了提高多相机一维标定的精度,提出了一种基于归一化算法的分层逐步标定法,由基本矩阵获得射影投影矩阵,进而转换成度量投影矩阵。对标定物图像特征点的坐标进行归一化预处理,以提高标定精度,同时又保持线性方法快速、易实现的优点。在所提标定方法中,一维标定物可自由运动,不受场地环境约束,使用灵活。通过仿真和真实实验,验证了归一化特征点坐标可以显著提高标定结果的精度和稳健性。In order to improve the accuracy of one-dimensional(1 D) multi-camera calibration, a gradual calibration method based on the normalization algorithm is proposed, where the projective projection matrices are first obtained from the fundamental matrices and then transformed into the metric projection matrices. The coordinates of the image feature points of the calibration object are pre-processed by normalization, improving the accuracy of calibration and simultaneously maintaining the advantages of fast and easy implementation of the linear method. In the proposed calibration method, the 1 D calibration objects can move freely without restriction of the site environment and are flexible to use as well. The simulation and real experiments demonstrate that the normalized feature point coordinates can substantially improve the accuracy and robustness of calibration results.

关 键 词：机器视觉 一维标定物 多相机标定 归一化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]