线结构光三维传感器扫描方向标定方法

A Scanning Direction Calibration Method of Line-Structured Light Three-Dimensional Sensors

线结构光三维传感器需要结合扫描机构才能对物体进行三维重构,在使用前需要对扫描方向进行标定。由于各个标定图像的清晰度不同,传统标定方法会多次引入噪声,降低了标定精度。为了减小由图像清晰度不同多次引入的噪声,本文提出了基于联合估计的扫描方向标定方法。在标定过程中,需要使用位移台将平面靶标移动一个固定的距离,使每个拍摄位置处的靶标相对相机坐标系的旋转矩阵相同,同时平移向量的变化由位移台的运动步长约束。通过对旋转矩阵和平移向量增加约束,将平面靶标上的二维特征点拓展为三维特征点;联合所有标定图像进行统一的单应性估计,减小了由图像清晰度不同多次引入的噪声。通过测量量块尺寸进行了验证实验,实验结果表明:所提方法的测量误差相比传统方法减小了约30%,而且所提方法具有更好的重复性。所提方法实现了线结构光三维传感器扫描方向的高精度标定,减小了传感器三维重构的误差。

Objective In a visual measurement system,the scanning mechanism is used to move the vision sensors or the measured object to expand the measurement range,which is known as translation scanning.The measurement range of a single image,particularly for a linestructured light threedimensional(3D)sensor,is only the light stripe formed by the intersection of the light plane and object.To achieve a 3D reconstruction of the measured object,a scanning mechanism must scan the entire surface of the light plane.In practical applications,linestructured light 3D sensors and translation scanning mechanisms are used in combination to measure flat objects and in defect detection,quality control,geometric dimension measurement,positioning,and other applications.However,before use,it is necessary to unify the coordinate systems of the scanning mechanism and sensor,that is,to calibrate the translation direction of the scanning mechanism.In the traditional calibration method,the checkerboard plane target is fixed,sensor is moved along the scanning direction,and camera captures target images.Subsequently,the extrinsic camera parameters corresponding to each image are estimated separately,and the 3D coordinates of the same feature point on each target image are calculated in the camera coordinate system.Finally,the 3D coordinates of the same feature point are fitted with a straight line,and the straight line’s direction vector is the scanning direction.Because of the different sharpness of the target images captured at different positions,the corresponding camera extrinsic parameters contain different noises for each image separately,introducing noise several times when calculating the 3D coordinates of feature points and then reducing the calibration accuracy.Methods First,the study analyzed and verified the disadvantages of the traditional calibration method,which introduces noise several times.In the verification experiment,the target was only translated by the highprecision stage;the orientation of the plane target relative to the camera remained unchanged,and the translation distance of the target was measured using the laser interferometer as the reference value.The ideal rotation matrix for each capture position should be identical to the initial capture position.However,the experimental results show that the rotation matrices estimated by the traditional methods are different(Fig.3),and the estimated value of the target translation differs significantly from the reference value(Fig.4).This proves that the traditional method introduces noise several times,decreasing the accuracy of the scanningdirection calibration.To reduce noise caused by different image sharpnesses,a scanning direction calibration method based on joint estimation was proposed.The scanning direction vector was added to the camera imaging model,and in the calibration process,the translation stage was used to move the plane target at a fixed distance.Therefore,the rotation matrix of the target relative to the camera coordinate system at each capture position remains unchanged,and the change in the translation vector is constrained by the translation stage’s movement distance.By adding constraints on the rotation matrix and translational vector,the 2D feature points on the plane target are expanded to 3D feature points,which are all combined for one homography estimation,and the noise caused by different image sharpnesses is reduced.Results and Discussions To verify the proposed calibration method,the two methods were used for repeated calibration,and the repeatability of the two method’s results was compared to verify the calibration algorithm’s precision.Ten repeated experiments showed that the direction vectors based on the joint estimation had a smaller standard deviation and higher precision.Following calibration,the size of the measuring block was measured to verify the algorithm’s accuracy.The size of the gauge block was 25 mm,measurement error of the traditional method was 32.7μm,and measurement error of the method proposed in this study was 25.2μm(Fig.8).To verify the stability of the calibration method,a total of ten repeated calibrations were performed,and the results of the ten repeated calibrations were used to measure the size of the gauge blocks.The average measurement error of the proposed method is 25.0μm,while the average measurement error of the traditional method is 35.7μm.Compared with the traditional method,the measurement error of this method was reduced by an average of 30%(Fig.9).According to the experimental results,the proposed calibration method has a higher scanning direction calibration accuracy and good robustness.Conclusions To improve the 3D reconstruction accuracy of linestructured light 3D sensors,this study first analyzes and verifies the shortcomings of the traditional scanning direction calibration method,which introduces noise several times,and then proposes a scanning direction calibration method based on joint estimation.The 2D feature points on the plane target are expanded to 3D feature points by adding constraints to the rotation matrix and translation vector,which realizes one joint homography estimation for all the calibration images and improves the calibration accuracy of the scanning direction.The experimental results show that this method improves the scanning direction calibration accuracy and reduces the sensor’s 3D reconstruction error.