Calibration of galvanometric laser scanners using statistical learning methods

Abstract

Galvanometric laser scanners can be used for optical tracking. Model-based calibration of these systems is inaccurate and not adaptable to variations in the system. Therefore, a calibration method based on statistical learning methods is presented which directly incorporates the triangulation problem. We investigate linear regression as well as Artificial Neural Networks. The results are validated using (1) the cross-validated prediction accuracy within the calibration space, and (2) plane reconstruction accuracy. All statistical learning methods outperformed the model-based approach leading to an improvement of up to 74% for the cross-validated 3D root-mean-square error and 70-74% for the plane reconstruction. While the neural network achieved mean errors below 0.5 mm, the linear regression results suggest a good compromise between accuracy and computational load.