Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

Junchao Tu,Liyan Zhang

doi:10.3390/s18010197

Abstract

A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

Highlights

A typical galvanometric laser scanner consists of two rotatable mirrors driven by two limited-rotation motors, respectively
The incoming laser beam are deflected by the mirrors, the orientations of which are uniquely determined by the control voltages applied to the two motors, so there exists a one-to-one mapping between the two input voltage signals and the outgoing laser beam
Within the extreme learning machine framework, the system model calibration is completed by only solving a linear system, which avoids the long training time required by most machine learning methods

Summary

Introduction

A typical galvanometric laser scanner consists of two rotatable mirrors driven by two limited-rotation motors, respectively. The incoming laser beam are deflected by the mirrors, the orientations of which are uniquely determined by the control voltages applied to the two motors, so there exists a one-to-one mapping between the two input voltage signals and the outgoing laser beam. Due to the good characteristics of high deflection speed, high positioning repeatability, low price and concise structure, galvanometric laser scanning (GLS) systems are broadly used as the key component of a variety of devices for diverse applications, such as laser marking [1,2], laser projection [3,4], optical metrology [5,6], material processing [7,8,9,10], medical imaging [11,12], etc.

Methods

Results

Discussion

Conclusion