Automated Eye-in-Hand Robot-3D Scanner Calibration for Low Stitching Errors

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).