移动机器人定位图像匹配的快速局部特征算法  

Fast local feature algorithm applied to mobile robot localization image matching

作　　者：刘萍萍[1,2] 赵宏伟[1,2] 臧雪柏[1] 王慧[1] 

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]吉林大学符号计算与知识工程教育部重点实验室长春130012

出　　处：《仪器仪表学报》2009年第8期1714-1719,共6页Chinese Journal of Scientific Instrument

基　　金：高等学校博士学科点专项科研基金(20050183032);吉林省教育厅科学基金(2004150)资助项目

摘　　要：利用定位图像的局部特征进行移动机器人导航和定位是近年来该领域的研究热点。针对经典局部特征提取和描述算法实时性不好,提出一种快速局部特征(fast local feature,FLF)的检测子和描述子算法。利用离散尺寸的均值滤波器估算LOG算子构建图像尺度空间,建立尺度和旋转不变的检测子。在尺度相关的邻域中利用规格化后的像素强度作为关联信息,建立局部邻域的描述子。模拟实验中,利用优选参数的FLF与经典SIFT算法对比,在识别率相当的条件下,FLF的运行时间是SIFT的1/3。在标准评估图片和移动机器人平台拍摄的定位图片构成的数据集下,FLF的匹配效果好于经典的SIFT和SURF算子。因而FLF是一种适合于实时应用的快速局部特征。Using local features of localization images to navigate and localize is a hotspot in recent mobile robot research. A novel, fast local feature in real time localization image matching is proposed in this article, called FLF （fast local feature）. We use the mean filters in disperse sizes to approximate LOG to build the image scale space. Then a fast local character descriptor is proposed. Normalized pixel intensity is computed on characteristic scale and used as the contextual information to describe the local neighbor pixel intensity variations to the central point. In simulation experiment, the run time of FLF using optimal selection parameters is about 1/3 of that of classical SIFF arithmetic under comparative recognition rate. In another matching experiment, within which the datasets are composed of standard evaluation images and images taken by mobile robot platform, performance evaluations show that FLF generally outperforms SIFT and SURF operators in image scale, rotation, illumination and blur transformations. Therefore, FLF is suitable for real-time image matching applications.

关 键 词：局部特征 图像匹配 尺度空间 物体识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]