摘要
针对基于传统特征点匹配的双目视觉测量方法误匹配率高和测量精度低的问题,提出了一种基于ORB(Oriented Fast and Rotated Brief)特征与随机抽样一致性(RANSAC)的双目测距方法。首先,基于双目位置信息的极线约束与基于汉明距离的特征匹配方法删除误匹配点,得到初步筛选的正确匹配点对。然后,基于k维树的近邻点顺序一致性约束方法筛选出初始内点集合,并采用迭代预检验方法提高RANSAC的匹配速度。最后,为了提升测量精度,采用二次曲面拟合得到亚像素点视差并计算实际距离。实验结果表明,本方法可以有效提高特征的匹配速度及测量精度,满足实时测量的要求。
Aiming at the problems of high mismatch rate and low measurement accuracy of the traditional binocular vision measurement method based on feature point matching,a binocular ranging method based on ORB(Oriented Fast and Rotated Brief)feature and random sample consensus(RANSAC)is proposed in this paper.First,the method of combining epipolar constraint based on binocular position information and feature matching based on Hamming distance is used to delete mismatched points,get the correct matching point pair initially screened.Then,the sequential consistency constraint method of nearest neighbors based on k-dimension tree is used to screen out the initial interior point set,and the iterative pre-check method is used to improve the matching speed of RANSAC.Finally,in order to improve measurement accuracy,the sub-pixel point disparity is obtained by quadric surface fitting,and calculated actual distance.Experiments show that the method can effectively improve the matching speed and measurement accuracy of features,and meet the requirements of real-time measurement.
作者
化春键
潘瑞
陈莹
Hua Chunjian;Pan Rui;Chen Ying(School of Meclanical Engineering,Jiangm.an Universitg,Wuaei,J iangsu 214122,China;Jiangsu Key Laborwtory of Ad xwnced Fool Manufacturing Equipment&Technology,Wuei,Jiangsu 214122,China;School of luternet of Things Engineering,Jiangnan Universitgy,Wuari,Jiangsu 214122,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第22期358-365,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61573168)
关键词
机器视觉
随机抽样一致性
极线约束
k维树
顺序一致性约束
亚像素点
machine vision
random sample consensus
epipolar constraint
k-dimension tree
sequential consistency constraint
sub-pixel
