KEYWORDS: Point clouds, Calibration, Image segmentation, Cameras, 3D modeling, 3D image reconstruction, Image registration, Convolution, Image processing, Camera calibration
The pipeline welding robot is limited by mechanization and programmed operation, and it is difficult to adjust the welding mode according to the actual situation of the pipeline. It has the disadvantages of long welding teaching cycle and complicated manual operation. The welding quality of the pipeline is generally low, and the basic stability of the production quality is difficult to guarantee. Based on the research of machine vision, three-dimensional scanning of welds is carried out to obtain three-dimensional point cloud data to achieve high-precision three-dimensional reconstruction of pipeline welds, which can simplify the operation process, reduce labor costs, improve production quality, and make welding robots adapt to the development needs of flexible manufacturing field. Therefore, a set of welding robot system is designed. In this paper, the depth map is obtained by SGBM, and the camera pose is obtained and optimized by the image pose + point cloud registration method to realize the three-dimensional reconstruction of the pipeline. Based on the semantic segmentation of pipeline weld images in complex scenes and the three-dimensional automatic measurement of pipeline reconstruction welds, welds were obtained based on the semantic segmentation model of lightweight backbone network Mobilenetv2+Unet, and the width of welds was measured by the centerline method.
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