Three-dimensional reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface

亚像素渲染 计算机视觉 结构光 直线(几何图形) 计算机科学 投影(关系代数) 人工智能 曲面重建 曲面(拓扑) 点云 算法 像素 数学 几何学
作者
Yue Wang,Xueyou Han,Rui Jing,Hailan Zhang,Lei Yin,Xuefeng Zhang,Xiangjun Wang
出处
期刊:Journal of the Optical Society of America [Optica Publishing Group]
卷期号:40 (11): 2096-2096 被引量:8
标识
DOI:10.1364/josaa.495352
摘要

Stereo vision technology based on line structured light can effectively solve the problem of a three-dimensional (3D) reconstruction of a smooth surface. A method for 3D reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface is presented in this paper. The subpixel coordinates of the light strip centers of the line structured light are obtained by the Steger algorithm while the binocular module moves along the guide rail, and the polar constraint is used to achieve the matching of the extracted light strip centers. As a result, the 3D coordinates of the light strip centers in each location can be calculated because of the known interior and external parameters of the binocular module. To obtain the 3D point cloud data of the entire surface, a relative pose optimization method with respect to the initial frame is proposed, which accurately estimates the pose of the cameras in each location with respect to that in the initial location and unifies the 3D coordinates of the light strip centers in each location to the datum coordinates. The relative pose optimization method first estimates the rough values by using the direct linear transform method, and then iteratively calculates the refined solutions based on the principle of minimizing the re-projection errors. Simulation data and substantial experimental results validate the effectiveness of our method. Our method is compared to the direct linear transform method and the frame-by-frame transfer method, and the root mean square error (RMSE) of the distance from 3D point cloud to fitted plane is used to evaluate the 3D reconstruction accuracy. The repeatability experiment shows that the RMSE from our method is as low as 0.83 mm.
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