Automatic attitude measurement of laser tracker based on deep learning and PnP model

In view of the urgent demand for attitude measurement in high-end manufacturing applications, such as aerospace and automobile assembly, a fast and high-precision attitude measurement method for a laser tracker was proposed. The method employed deep learning in conjunction with the visual PnP model to realize automatic attitude measurement of the laser tracker. The correspondence between 3D feature points and 2D feature points required by the traditional PnP model were directly determined through a feature extraction network designed to extract high-dimensional features. The joint probability distribution between feature vectors was determined using optimal transmission theory to complete the matching of 3D-2D feature points. Subsequently, Ransac-P3P combined with EPnP algorithm was used to obtain high-precision attitude information; Based on this, the Jacobian matrix of PnP solution process was calculated using implicit differential theory, and the PnP attitude solution model was integrated into the network to guide the training of the network. The complementary advantages of strong depth network matching ability and high attitude solution accuracy of the PnP model improved the solution accuracy of the network. In addition, a dataset with rich annotation information was used to train the attitude measurement network for the laser tracker. Finally, an attitude measurement test was conducted using a high-precision two-dimensional turntable. The experimental results show that the calculation error of pitch angle is less than 0.31°, the rolling angle error is less than 0.03°, and the single measurement takes approximately 40 ms. The proposed method can potentially be applied to attitude measurement scene of the laser tracker. © 2022, Science Press. All right reserved.