Tu Junchao, Zhang Liyan
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China.
Sensors (Basel). 2018 Jan 12;18(1):197. doi: 10.3390/s18010197.
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.
提出了一种用于振镜式激光扫描（GLS）系统校准问题的新解决方案。在机器学习框架下，我们构建了一个单隐层前馈神经网络（SLFN）来表示GLS系统，该网络将GLS系统驱动器处的数字控制信号作为输入，并将相应出射激光束的空间矢量作为输出。借助移动机构和双目立体系统获得训练数据集。使用极限学习机（ELM）以闭式形式有效地求解SLFN的参数。通过定量分析相对于SLFN中隐藏神经元数量的回归精度，我们证明可以从很宽的区间中安全地选择合适数量的隐藏神经元，以保证良好的泛化性能。与传统的模型驱动校准相比，所提出的校准方法不需要复杂的建模过程，并且更准确、更稳定。由于网络的输出是出射激光束的空间矢量，与现有的仅适用于激光三角测量问题的数据驱动校准方法相比，它花费的训练时间少得多，并且可以为激光投影和三维重建提供统一的解决方案。分别进行了校准实验、投影实验和三维重建实验来测试所提出的方法，并获得了良好的结果。
