Abstract: In order to solve the problem of low positioning accuracy of binocular vision system, a binocular vision target positioning method based on coarse-fine stereo matching was proposed. The coarse-fine matching strategy was adopted in the proposed method, firstly the random fern algorithm based on Canny-Harris feature points was used to identify the target in the left and right images at the stage of coarse matching, and the center points of target rectangular regions were extracted to achieve the center matching. Then, a binary feature descriptor based on image gradient information was established at the stage of fine matching, and the right center point obtained by center matching was used as an estimated value to set a pixel search range, in which the best matching point of left center point was found. Finally, the center matching points were substituted into the mathematical model of parallel binocular vision to achieve target positioning. The experimental results show that the proposed method has the positioning error controlled in 7 mm within 500 mm distance, and the average relative positioning error of 2.53%. Compared with other methods, the proposed method has the advantages of high positioning accuracy and short running time.

Key words: binocular stereo vision, stereo matching, random fern, binary feature descriptor, similarity measurement, camera calibration

摘要： 针对双目视觉系统定位精度较低的问题，提出一种基于粗-精立体匹配的双目视觉目标定位方法。该方法采用粗-精匹配策略：在粗匹配阶段使用基于Canny-Harris特征点的随机蕨算法对左右图中的目标进行识别，提取目标矩形区域的中心点，实现中心匹配；在精匹配阶段建立一种基于图像梯度信息的二值特征描述子，将中心匹配得到的右中心点作为估计值，设定像素搜索范围，于该区域中找出左中心点的最佳匹配点。最后，将得到的中心点匹配对代入平行双目视觉的数学模型中，实现目标定位。实验结果表明，在500 mm距离范围内，所提出定位方法的定位误差控制在7 mm内，平均相对定位误差为2.53%，相比其他方法具有定位精度高、运行时间短的优点。

关键词: 双目立体视觉, 立体匹配, 随机蕨, 二值特征描述子, 相似性度量, 摄像机标定