计算机工程2011，Vol.37Issue(20)：194-196,199,4.DOI:10.3969/j.issn.1000-3428.2011.20.067
基于特征匹配和卡尔曼滤波的机器人视觉稳像
Robot Vision Image Stabilization Based on Feature Matching and Kalman Filtering
摘要
Abstract
Video stabilization is the key of robot vision. This paper establishes an image affine kinematics model with 6 parameters and its recurrence relations. Kanade-Lucas-Tomasi(KLT) feature matching method is designed based on grads. Optimization of sum of absolute difference is used to match feature points. Through analysis of over-determined image motion equations, observation model of intended motion parameters is derived, and the least squares algorithm is used to solve equations. Through reverse computing of kinematics model using filtered parameters, jitter is compensated and stabilized images are achieved. Experiments on autonomous mobile robot test-bed show that the feature points are uniform distributed and matching is faster by using KLT algorithm with sub-regional fast computing, and the relative parameters filter effect is smoother than the absolute parameters filtering.关键词
KLT算法/特征匹配/运动模型/卡尔曼滤波/稳像Key words
Kanade-Lucas-Tomasi(KLT) algorithm/ feature matching/ motion model/ Kalman filtering/ image stabilization分类
信息技术与安全科学引用本文复制引用
徐崟,王斌锐,金英连..基于特征匹配和卡尔曼滤波的机器人视觉稳像[J].计算机工程,2011,37(20):194-196,199,4.基金项目
国家自然科学基金资助项目(50905170) （50905170）
浙江省自然科学基金资助项目(Y1090042) （Y1090042）
