Automated Eye-in-Hand Robot-3D Scanner Calibration for Low Stitching Errors
Harikrishnan Madhusudanan, Xingjian Liu, Wenyuan Chen, Dahai Li, Linghao Du, Jianfeng Li, Ji Ge, Yu Sun
Abstract
A 3D measurement system consisting of a 3D scanner and an industrial robot (eye-in-hand) is commonly used to scan large object under test (OUT) from multiple fieldof-views (FOVs) for complete measurement. A data stitching process is required to align multiple FOVs into a single coordinate system. Marker-free stitching assisted by robot’s accurate positioning becomes increasingly attractive since it bypasses the cumbersome traditional fiducial marker-based method. Most existing methods directly use initial Denavit-Hartenberg (DH) parameters and hand-eye calibration to calculate the transformations between multiple FOVs. Since accuracy of DH parameters deteriorates over time, such methods suffer from high stitching errors (e.g., 0.2 mm) in long-term routine industrial use. This paper reports a new robot-scanner calibration approach to realize such measurement with low data stitching errors. During long-term continuous measurement, the robot periodically moves towards a 2D standard calibration board to optimize kinematic model’s parameters to maintain a low stitching error. This capability is enabled by several techniques including virtual arm-based robot-scanner kinematic model, trajectory-based robot-world transformation calculation, nonlinear optimization. Experimental results demonstrated a low data stitching error (< 0.1 mm) similar to the cumbersome marker-based method and a lower system downtime (< 60 seconds vs. 10-15 minutes by traditional DH and hand-eye calibration).
BibTeX
@inproceedings{icra2020_automatedeyeinha,
title = {Automated Eye-in-Hand Robot-3D Scanner Calibration for Low Stitching Errors},
author = {Harikrishnan Madhusudanan and Xingjian Liu and Wenyuan Chen and Dahai Li and Linghao Du and Jianfeng Li and Ji Ge and Yu Sun},
booktitle = {ICRA 2020},
year = {2020}
}