摘要
提出了一种基于核主分量分析(PCA)正则化的机器人实时定位算法。此算法以半监督学习完成离线训练,首先,以机器人在其预置运动路径上采集到的畸变图像中的稀疏目标面积为观察数据,将部分标定数据的坐标作为其标签,然后以核PCA所揭示的低维视觉流形为正则化约束条件,运用最小二乘方法估计无标签数据坐标。在线定位阶段,利用调和函数估计在线采集到的数据坐标,从而实现基于无标定单目视觉传感器的机器人在线定位。实验结果表明,和其他常规的定位方法相比较,提出的实时定位算法的计算复杂性小、定位精度高、实时性强,能够满足工业机器人和医疗服务机器人等方面的实时定位要求。
This paper presents a new approach to real-time robot localization using kernel principal component analysis（PCA） regularization.The proposed algorithms are formulated as a semi-supervised learning during offline training.Firstly,sparse area features are extracted from the images captured by the camera mounted on the robot which moves along a predetermined path,and labeled a part of the data with their coordinates.Then,the coordinates of the unlabeled data are estimated by least squares with constraint of regularized low dimensional visual manifold in kernel PCA.In online localization stage,harmonic functions are employed to predict new data coordinates so that the real-time robot localization can be implemented using uncalibrated monocular vision.A series of experiments manifest that the proposed algorithms can outperform other conventional methods with low computational complexity,high localization accuracy and well real-time performance,so as to meet the real-time application requirements of industrial robots and medical service robots.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2010年第1期153-162,共10页
Acta Optica Sinica
基金
国家863计划(2006AA04Z207)
国家自然科学基金(60875072)
教育部博士点基金(20060006018)
中澳国际合作项目(2007DFA11530)资助课题
关键词
机器视觉
流形正则化
核主分量分析
面积特征
machine vision manifold regularization kernel principal component analysis area feature
