Transformer Connection Terminal Registration Based on Objective Function

Abstract

Abstract In recent years, the traditional ICP algorithm has high requirements for the initial position of point clouds, and low registration ability for point clouds with low overlap. The Microsoft Kinect depth sensor was used to obtain the point cloud data of the target object from the real scene. Then, pre-processing such as point cloud segmentation, filtering and down sampling. In the coarse registration, the feature point sampling consistency algorithm was used to make the point cloud obtain a better initial position. Finally, a point-to-surface ICP algorithm optimized by linear least squares was proposed in the fine registration. The experimental results show that the root mean square error of the improved algorithm is 0.761mm and the time is 52.32ms. Compared with the ICP algorithm based on SIFT feature points and the improved ICP algorithm based on feature point sampling consistency, the registration accuracy of the improved algorithm is increased by 21.0% and 43.3%, and the speed is increased by 18.9% and 30.2%.