A Tracker Pose Optimization Method for Robotic Measuring System Based on Spatial Distance Constraints

In large-scale metrology (LSM), the transformation of the tracker base frame (TBF) is a predominant method to enlarge the field of view (FOV) of the tracking sensor for full-field 3D measurements. Nevertheless, such a process will introduce cumulative errors and significantly diminish the global point cloud alignment accuracy. To address this problem, we propose a novel tracker pose optimization method for TBF transformation. A pose graph optimization (PGO) model based on spatial distance constraints is implemented to improve the tracker pose accuracy. We also adopt a robust coefficient and a damping factor to simplify the experimental process and stabilize the convergence results. Simulations and experiments on high-speed train surfaces are conducted to validate our method's accuracy and effectiveness. The results indicate that our optimization method outperforms two existing methods in spatial positioning accuracy and point cloud alignment accuracy, which showcases its practical applicability and superiority in manufacturing scenarios.