改进的ORB-FLANN煤矸石图像高效匹配方法  

Improved ORB-FLANN efficient matching method for coal gangue image

作　　者：马宏伟[1,2] 周文剑 王鹏 张烨 赵英杰 王赛赛 李烺 MA Hongwei;ZHOU Wenjian;WANG Peng;ZHANG Ye;ZHAO Yingjie;WANG Saisai;LI Lang(School of Mechanical Engineering,Xi’an University of Science and Technology,Xi’an 710054,China;Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control,Xi’an 710054,China)

机构地区：[1]西安科技大学机械工程学院,陕西西安710054 [2]陕西省矿山机电装备智能检测与控制重点实验室,陕西西安710054

出　　处：《煤炭科学技术》2024年第1期288-296,共9页Coal Science and Technology

基　　金：国家自然科学基金面上资助项目(51975468);陕西省自然科学基础研究计划资助项目(2023-JC-YB-362);陕西省教育厅自然科学研究资助项目(23JK0548)。

摘　　要：针对煤矸石分拣机器人分拣煤矸石时,带式输送机输送带打滑、跑偏以及带速波动造成的目标煤矸石位姿变化,从而导致抓取失败或空抓漏抓等问题,提出了一种改进的ORB-FLANN (Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)煤矸石识别图像与分拣图像高效匹配方法。提出改进ORB的特征点检测方法对煤矸石识别图像与分拣图像进行特征点检测,实现快速检测图像特征点;提出改进FLANN匹配算法对图像特征点进行匹配,实现煤矸石识别图像与分拣图像高效匹配。针对传统ORB方法对煤矸石图像特征检测时间长、重复率低问题,提出了改进ORB特征检测方法,提高了图像特征点检测速度和重复率;针对传统FLANN匹配方法对煤矸石图像匹配精确率低问题,提出了融合PROSAC算法的改进FLANN匹配方法,剔除错误特征匹配点对,提高了图像匹配的精确率。在自主研发的双机械臂桁架式煤矸石分拣机器人试验平台上应用文中方法、SURF特征匹配方法、HU不变矩匹配方法、SIFT特征匹配方法和ORB特征匹配方法分别进行了不同带速、尺度、旋转角度条件下的煤矸石匹配试验,结果表明:本方法的匹配率为98.2%,匹配时间为141 ms,具有匹配率高、实时性好以及鲁棒性强等特点,能够满足煤矸石识别图像与分拣图像高效精准匹配的要求。In order to solve the problem of grasping failure or missing grasping due to the change of target gangue position and posture caused by belt slip,deviation and belt speed fluctuation of belt conveyor when the gangue sorting robot sorts gangue,an improved ORB-FLANN efficient matching method of gangue recognition image and sorting image is proposed.An improved ORB feature point detection method is proposed to detect the feature points in the recognition image and sorting image of coal gangue,so as to realize fast detection of image feature points;An improved FLANN matching algorithm is proposed to match the image feature points to achieve efficient match-ing between the recognition image of coal gangue and the sorting image.Aiming at the problem of long time and low repetition rate of tra-ditional ORB method for coal gangue image feature detection,an improved ORB feature detection method is proposed to improve the speed and repetition rate of image feature point detection;Aiming at the low accuracy of traditional FLANN matching method for coal gangue image matching,an improved FLANN matching method integrating PROSAC algorithm is proposed to eliminate the wrong fea-ture matching point pairs and improve the accuracy of image matching.The method,SURF feature matching method,HU moment invari-ant matching method,SIFT feature matching method and ORB feature matching method are applied on the experimental platform of the double mechanical arm truss type gangue sorting robot independently developed by the team to carry out gangue matching experiments un-der different belt speeds,scales and rotation angles.The results show that the matching rate of the method in this paper is 98.2%,and the matching time is 141 ms.It has the characteristics of high matching rate,good real-time performance and strong robustness,It can meet the requirements of efficient and accurate matching of gangue recognition image and sorting image.

关 键 词：煤矸石分拣机器人 煤矸石识别图像 煤矸石分拣图像 特征检测 特征匹配 

分 类 号：TD67[矿业工程—矿山机电]