摘要
低成本双目视觉系统通常采用价格低廉的非测量相机,其视觉定位精度较低.若需获得较高的视觉定位精度,可通过算法来弥补或减少定位误差.本文阐述了基于H--S(hue-saturation)直方图反向投影结合特征点提取的双目视觉定位算法,可提取左右摄像头图像特征点,并用左图特征点和极线约束对右图特征点进行修正,从而可实现双目视觉定位.在此基础上,为了进一步提高定位精度和减少计算量,采用了快速鲁棒性特征(SURF)算法提取左右图像的兴趣点,并利用间接立体匹配法改进了右图特征点提取.实验结果表明,在低成本视觉定位系统中采用该算法可以有效地提高定位精度.
In low-cost binocular vision system, an inexpensive non-measure camera usually results in a low visual positioning accuracy. To obtain higher visual positioning accuracy, there exist some algorithms to reduce or compensate positioning errors. A binocular vision positioning algorithm based on H-S （hue-saturation） histogram back-projection combined with feature point extraction is proposed in this paper. In this algorithm, the H-S histogram back-projection method is employed to extract the feature points of images from the left and the right cameras; and then, the feature points of the left image and the epipolar constraints are employed to correct the feature points of the right image, to achieve binocular vision positioning. In order to further improve the positioning accuracy and reduce the cost of computation, the SURF （speed up robust feature） algorithm is used to extract the interest points of both left and right images, and an indirect stereo matching approach is employed to improve the feature point extraction of the right image. Experiment results show that for the low-cost visual positioning system this algorithm can effectively improve the positioning accuracy.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2014年第5期614-623,共10页
Control Theory & Applications
基金
国家自然科学基金资助项目(61175093)
关键词
双目视觉
目标定位
立体校正
反向投影
SURF算法
特征提取
binocular vision
object position
stereo rectification
back-projection
SURF algorithm
feature extraction
