基于改进的加速鲁棒特征算法的工件定位方法

Workpiece Positioning Method Based on Improved Speeded up Robust Features Algorithm

为了解决传统的图像处理算法识别现场获得的工件图像速度慢且匹配效果较差等问题,通过对工件图像的识别方法进行研究,提出了一种改进的加速鲁棒特征(SURF)算法可以实现工件准确、实时的定位。该算法基于加速分割测试特征检测器(FAST)对SURF算法的特征提取方式进行改进,首先利用FAST提取特征点,然后通过SURF算法生成特征点描述子,使用主成分分析算法(PCA)对描述子进行降维。随后以欧式距离作为相似性度量进行粗匹配,再采用随机抽样一致算法(RANSAC)剔除误匹配点。最后结合双目视觉技术得到工件空间位置坐标。实验结果表明:本文提出的算法在运行时间上相比传统SURF算法减少80%,同时提高了匹配的精度。达到了准确、实时的工件定位目的。

In order to solve the problem that the traditional image processing algorithm recognizes that the workpiece image obtained on the spot is slow and the matching effect is poor,the recognition method of the workpiece image was studied,and an improved speeded up robust features( SURF) algorithm was proposed to realize accurate and real-time positioning of the workpiece. The algorithm improved the feature extraction method of SURF algorithm based on features from accelerated segment test detector( FAST). Firstly,feature points were extracted by FAST,then feature point descriptors were generated by SURF algorithm,and the principal component analysis algorithm( PCA) was used to reduce the dimension of the descriptor. Then the euclidean distance was used as the similarity measure for rough matching,and then the random sampling agreement algorithm( RANSAC) was used to eliminate the mismatched points. Finally,combined with binocular vision technology,the workpiece spatial position coordinates were obtained. The experimental results show that the proposed algorithm reduces the running time by 80%compared with the traditional SURF algorithm,and improves the matching accuracy,achieving accurate and realtime workpiece positioning.