Design and analysis of full-scale scanning system for curved glass based on motion and 3D features.

In recent years, mobile phones with glass curved screens have become more and more widely used. The irregular shape of the curved screen and the light transmittance characteristic of the glass have brought great challenges to its automatic defect detection. Aiming at the defect detection of the glass cover of the curved screen, this paper designs a full-scale scanning system by combining motion and three-dimensional (3D) features. First, a scanning system is constructed, and a geometric error modeling method is proposed to improve the accuracy of the scanning system; second, based on the point cloud of the 3D glass cover obtained by the scanning system, a point cloud registration method is presented by integrating the motion and 3D features; finally, the laser tracker is further used to calibrate the scanning system to analyze the mechanical error. Experimental results show that the introduction of straightness error and perpendicularity error can effectively solve the mismatch and fault problems of point cloud registration, and improve the accuracy of the scanning system. In addition, the registration method proposed in this paper can effectively reconstruct the complete point cloud of 3D glass cover for detection. The reconstruction accuracy of the plane part can reach 0.031 mm, and that of the curved part can reach 0.091 mm.

Three-dimensional reconstruction method based on bionic active sensing in precision assembly.

With the prevailing application of new materials and the higher requirements for the quality and efficiency of production in the equipment manufacturing industry, traditional assembly methods can hardly meet the needs of large-scale production, especially in the field of high-precision assembly. Robot assembly guided by visual perception has become the key of the research in the field of engineering technology. It requires higher accuracy of robot visual perception and the control over force, position and so on. However, in 3C assembly, most products are made of transparent materials such as glass. Because of the transparency and specular reflection of the surface, 3D reconstruction of transparent objects is a very difficult problem in computer vision, in that the traditional visual perception methods could not be accurate enough. The present research proposes a bionic active sensing algorithm for 3D perception and reconstruction and realizes high-precision 3D by applying the registration algorithm. The purpose is to solve the problems existing in the traditional visual perception method, such as difficulties in achieving active sensing, low accuracy of point clouds registration, and complex computation. The results of the experiments show that the present method is efficient and accurate in 3D reconstruction. It reduces the planar reconstruction error to 0.064 mm and the surface reconstruction error to 0.177 mm.