Real- time high-precision model reconstruction based on global optimization

Three-dimensional(3D)shape measurement plays an important role in advanced manufacturing, aerospace,biomedicine and other fields. With the advantages of high precision,full field of view,and noncontact,fringe projection profilometry is currently the most widely used optical three-dimensional measurement method. In order to obtain 360 degrees global three-dimensional information,it is usually necessary to place the object to be measured on the turntable,and obtain the global information of the object by continuous scanning and stitching. However,the traditional scanning and stitching are performed offline,resulting in slow reconstruction of the entire 3D model. Although the existing real- time point cloud registration methods can effectively improve the speed of point cloud scanning and stitching,the accuracy of real- time point cloud stitching is still limited by the motion state of the object under test. This paper optimizes and improves the above problems,and proposes a real-time high-precision model reconstruction method based on global optimization. Firstly,a fast point cloud registration algorithm from coarse to fine registration is introduced, and a point cloud initialization algorithm based on point cloud normal vector constraints is proposed on this basis,which can improve the stability and accuracy of the point cloud initial pose calculated during the rough registration process. Secondly,a graph optimization algorithm is introduced in the fine registration stage to obtain the optimal solution of the global point cloud pose,which further improves the accuracy of global point cloud registration. The experimental results show that the proposed method can achieve higher precision and stable global point cloud registration than the current real-time point cloud registration method. In particular,this method can still robustly complete the reconstruction of the 3D model,and the accuracy of the omni-directional model reconstruction reaches 84 mu m, especially for situations such as sudden changes in the speed of the measured object caused by factors such as jitter in the dynamic scene.