%0 Journal Article
%T 基于双PSD的三维测量系统的标定方法<br>Calibration of 3-D measurement system based on a double position sensitive detectors
%A 郑军
%A 李文庆
%J 清华大学学报（自然科学版）
%D 2018
%R 10.16511/j.cnki.qhdxxb.2018.26.023
%X 双目视觉已在机器人视觉、三维测量等多个领域得到广泛应用。但是，随着研究的不断深入，双目视觉的局限性也逐渐突显出来。例如测量复杂形面的三维形貌精度低，计算速度慢等。基于此，该文提出一种基于双光敏位置探测器（position sensitive detector，PSD）的三维测量方法。利用2个PSD从不同的角度捕获、跟踪激光点的方式来还原工件的三维信息，省去了传统双目视觉中的特征点识别和匹配部分，极大简化了三维测量模型。由于传统的标定方法不再适用，该文提出2种标定方案，改进的Faugeras标定加LM（Levenberg-Marquardt）优化方法和BP（back propagation）神经网络方法。通过对比实验分析，改进的Faugeras标定加LM优化的方法能达到更高更稳定的三维测量精度。<br>Abstract：Binocular vision systems have been widely used in many areas. Traditional calibration methods for binocular vision systems commonly use many complicated mathematical models, which result in low precision and speed. This paper presents a fast measurement method based on double position sensitive detectors (PSDs). Two detectors are aimed from different angles to detect the position of the laser point for the 3D measurement. The 3-D measurement is greately simplified by replacing a charge coupled device (CCD) with a PSD. Since this method is fundamentally different from traditional methods, the normal calibration methods are no longer applicable. Thus, this article presents two calibration methods respectively using an improved Faugeras calibration combined with Levenberg-Marquardt (LM) arithmetic optimization and a back propagation (BP) neural network. Tests show that the LM optimization gives better accuracy and stability.
%K 三维测量
%K 光敏位置探测器
%K Faugeras标定方法
%K LM(Levenberg-Marquardt)优化
%K BP(back propagation)神经网络
%K &lt
%K br&gt
%K 3-D measurement
%K position sensitive detector
%K Faugeras calibration
%K Levenberg-Marquardt arithmetic optimization
%K back propagation neural networks
%U http://jst.tsinghuajournals.com/CN/Y2018/V58/I4/411