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

ZHOU Daode; GAO Doudou; DONG Dengfeng; ZHOU Weihu; CUI Chengjun

doi:10.37188/OPE.20223009.1047

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

Modern Applied Optics | 更新时间：2026-09-11

- Automatic attitude measurement of laser tracker based on deep learning and PnP model
- Optics and Precision Engineering Vol. 30, Issue 9, Pages: 1047-1057(2022)
- 作者机构：
  
  1.中国科学院微电子研究所，北京100029
  2.中国科学院大学，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223009.1047
  CLC： TP391.4;TH744
- Received：04 March 2022，
  
  Revised：2022-03-16，
  
  Published：10 May 2022
- 稿件说明：
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周道德,高豆豆,董登峰等.基于深度学习和PnP模型的激光跟踪仪自动姿态测量[J].光学精密工程,2022,30(09):1047-1057.

ZHOU Daode,GAO Doudou,DONG Dengfeng,et al.Automatic attitude measurement of laser tracker based on deep learning and PnP model[J].Optics and Precision Engineering,2022,30(09):1047-1057.
周道德,高豆豆,董登峰等.基于深度学习和PnP模型的激光跟踪仪自动姿态测量[J].光学精密工程,2022,30(09):1047-1057. DOI： 10.37188/OPE.20223009.1047.

ZHOU Daode,GAO Doudou,DONG Dengfeng,et al.Automatic attitude measurement of laser tracker based on deep learning and PnP model[J].Optics and Precision Engineering,2022,30(09):1047-1057. DOI： 10.37188/OPE.20223009.1047.

摘要

针对航空航天、汽车装配等高端制造领域对姿态测量的迫切需求，提出一种面向激光跟踪仪的快速高精度姿态测量方法，利用深度学习结合视觉PnP模型实现了激光跟踪过程中被测件姿态的自动测量。针对PnP姿态求解模型所需的3D特征点和2D特征点之间的对应关系难以直接确定的问题，设计了一个特征提取网络用于提取特征点对应的高维特征，采用最优传输理论确定特征向量之间的联合概率分布，从而完成3D-2D特征点的自动匹配；使用Ransac-P3P结合EPnP算法对匹配好的3D特征点和2D像素点进行姿态求解，获得高精度的姿态信息；在此基础上，利用隐式微分理论计算PnP求解过程的雅克比矩阵，从而将PnP姿态求解模型集成到网络中并指导网络训练，实现了深度网络匹配能力与PnP模型姿态求解能力的优势互补，提高了解算精度。最后，制作了一个含有丰富标注信息的数据集，用于训练面向激光跟踪仪的姿态测量网络。基于高精度二维转台进行了姿态测量实验，结果表明，该方法在3 m处对俯仰角的测量精度优于0.31°，横滚角精度优于0.03°，单次测量耗时约40 ms，能够实现激光跟踪仪的高精度姿态测量。

Abstract

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.

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