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【摘 要】
：
考虑到无人机运动、机载摄像头采集效果、信号传输以及环境变化等因素,针对传统ORB特征匹配算法存在匹配精度低、鲁棒性差等问题,提出了一种基于稀疏光流法的改进ORB特征匹配算法.首先,采用高斯滤波对无人机采集的图像进行预处理,并采用Harris算子剔除特征点中伪角点,提高图像特征点的质量;其次,采用K-means++算法实现图像特征点的聚类处理,由此减少暴力匹配的计算量;在此基础上,将稀疏光流法引入ORB特征匹配算法,计算特征点的运动向量并根据估计特征点在待匹配图像中的二维坐标,实现剔除偏离聚类中心较远的特征
【机 构】
：
武汉科技大学机器人与智能系统研究院,武汉 430081;武汉科技大学机器人与智能系统研究院,武汉 430081;武汉科技大学冶金自动化与检测技术教育部工程研究中心,武汉 430081;武汉科技大学信息
【出 处】
：
组合机床与自动化加工技术
【发表日期】
：
2021年12期
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