基于局部信息熵和梯度漂移的双目视觉测量算法  

Algorithm for Binocular Vision Measurements Based on Local Information Entropy and Gradient Drift

作　　者：周书华 许四祥[1] 董晨晨 张浩 Zhou Shuhua;Xu Sixiang;Dong Chenchen;Zhang Hao(College of Mechanical Engineering,Anhui University of Technology,Maanshan 243032,Anhui,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243032

出　　处：《激光与光电子学进展》2023年第12期323-331,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51374007);安徽高校自然科学研究重点项目(KJ2020A0259);特种重载机器人安徽重点实验室开放基金(TZJQR005-2021)。

摘　　要：针对传统特征检测算法检测效率低、匹配正确率低和双目视觉测量精度不足等问题,提出一种基于局部信息熵和梯度漂移的双目视觉测量算法。首先,将图像划分成若干子区域,计算各子区域局部信息熵筛选出高熵区域,并利用oriented FAST and rotated BRIEF(ORB)算法检测特征点;其次,采用圆形邻域代替像素点,并对圆形邻域内各像素梯度幅值采用二维高斯加权的方式改进旋转不变local binary patterns(LBP);然后,与rotated binary robust independent elementary features(rBRIEF)融合生成新的描述子进行特征匹配;最后,提出梯度漂移方法,引入特征点次极大响应值作为辅助因素,结合极大响应值通过坐标迭代更新计算出理想特征点的精确坐标,解决提取特征点坐标不准确的问题,提高测量精度。实验结果表明:所提算法的平均匹配正确率较传统ORB算法提高37.51%,测量最低相对误差达到0.365%。An algorithm for binocular vision measurements based on local information entropy and gradient drift is proposed to solve the low detection efficiency,low matching accuracy,and insufficient binocular vision measurement accuracy of traditional feature detection algorithms.First,the image is divided into several sub-regions,the local information entropy of each sub-region is calculated to screen out the high-entropy regions,and the oriented FAST and rotated BRIEF(ORB)algorithm is used to detect feature points.Second,the circular neighborhood is used to replace the pixel points,and the gradient amplitude of each pixel in the circular neighborhood is improved using two-dimensional Gaussian weighting to improve the rotation invariant local binary patterns(LBP).Next,it is fused with the rotated binary robust independent elemental features(rBRIEF)to generate a new descriptor for feature matching.Finally,the gradient drift method is proposed.The sub-maximum response value of the feature point is introduced as the auxiliary factor.Combined with the maximum response value,the accurate coordinates of the ideal feature point are calculated through the iterative coordinate update,solving the inaccurate feature point coordinates and improving the measurement accuracy.The experimental results show that the average matching accuracy of the proposed algorithm is 37.51%higher than that of the traditional ORB algorithm,and the lowest relative measurement error is 0.365%.

关 键 词：机器视觉 双目视觉 局部信息熵 改进旋转不变local binary patterns 梯度漂移 测量精度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]