A Robust Simultaneous Calibration Method for Mobile Robotic Measurement System Based on Global-Photogrammetry Tracking

A mobile robotic measurement system combined with a mobile tracker (MRSMT) provides a promising solution for 3-D full-field measurement because the measurement range can be flexibly enlarged by changing the tracker location multiple times. In such systems, the scanner poses can be calculated by capturing tracking markers with the tracker. To accurately align point clouds obtained from different tracker locations, the scanner-to-marker matrix (SMM) and tracker-to-tracker matrix (TTM) must be calibrated. To the best of the authors’ knowledge, in existing calibration methods, SMM and TTM are separately calibrated using external markers, which leads to error accumulation and a complex calibration procedure. To address such problems, a coarse-to-fine calibration method for SMM and TTM is proposed. For coarse calibration, a closed-form model approximating ${\textit {AX} }={\textit {XB} }$ and a maker-based method calibrate SMM and TMM separately. Then, a simultaneous iterative optimization model based on Lie derivatives is adopted for fine calibration. Furthermore, to improve the calibration accuracy and robustness, the regularity term and Welsch robust kernel function are integrated into the optimization model. Finally, simulations and experiments are conducted to validate the proposed method. Both simulations and experiments demonstrate that the proposed calibration method has higher calibration accuracy and robustness compared with the existing calibration methods.