光笔视觉三维测量中光斑图像点立体匹配方法

Stereo Matching of Light-spot Image Points in Light-probe 3D Vision Measurement

针对光笔视觉三维测量中左右两幅图像中光斑图像点的立体匹配难题,引入SoftPosit算法将其转换为二维像点与三维物点对应关系未知情况下的位姿参数估计问题。通过将对应匹配和位姿估计融合在一起,进行捆绑迭代寻优,分别完成左右两幅图像中光斑图像点与空间光笔光斑的对应匹配,从而实现了左右两幅图像中对应光斑图像点的立体匹配。该方法基于模型,与空间点分布模式、点的数量及图像灰度无关,实验结果表明该方法切实可行,也可用于特征点的识别。

One of the difficulties in light-probe 3D vision measurement is the stereo matching of the light-spot image points in the left and right images. Therefore, the SoftPoist algorithm was adopted to transform the difficulty to a position and orientation estimation problem under the condition that the correspondences of the 2D image points and the 3D object points are unknown. By combining the correspondence problem and the position and orientation estimation problem together to perform bundle optimization, the matching of the light-spot image points and the spatial light-emitting-spots of the light-probe could be completed, respectively, in order to achieve the stereo matching of the light-spot image points in the left and right images. The proposed method is based on model and is not related to the spatial point distribution, point number and the image gray level. The experiment results show that the proposed method is feasible and can be also used for feature points recognition.