基于3D标定块的机器人与3D相机手眼标定研究

Research on Robot Hand-Eye Calibration Method Based on Three-Dimensional Calibration Block

近年来,随着工业4.0的提出和机器视觉的飞速发展,机器人搭配视觉系统实现智能化变为现实。视觉系统主要分为二维(2D)视觉和三维(3D)视觉,3D视觉有着高精度、自由度丰富、应用场景更多等优点,越来越受到市场的青睐。3D视觉与传统的2D视觉相比,可以获得更加全面的三维数据信息,且不受光照影响,但是X、Y方向的分辨率较低,因此基于棋盘格的传统手眼标定算法并不适用于3D相机与机器人进行手眼标定。因此,设计了一种基于3D标定块的机器人与3D相机的手眼标定方法。通过相机获得标定块的灰度信息,利用模板匹配算法分割标定块的各个平面,计算出平面中多个点在像素坐标系下的位置,同时获取该像素坐标的深度信息,拟合出标定块多个平面的法向量信息,通过多个平面相交求解出特征点位置。利用随机抽样一致性算法剔除错误特征点后,根据正确的特征点求解出机器人在不同姿态下获取标定块点云数据之间的转换关系,结合机器人当前坐标求解出手眼标定结果,并建立误差评价模型对结果进行误差分析。最后通过DENSON六自由度机械臂与康耐视EA-5000相机对此方法与利用标定板进行手眼标定的方法进行比较验证,结果显示此方法可以更快速、准确地完成标定。

With the proposal of Industry 4.0 and the rapid development of machine vision in recent years, the realization of an intelligent robot with a vision system has become a reality. The visual system is mainly divided into two-dimensional(2D) vision and three-dimensional(3D) vision. 3D vision has the advantages of high precision, rich degree of freedom, and more application scenes, and is increasingly favored by the market. Compared with traditional 2D vision, 3D vision can obtain more comprehensive 3D data information, and is not affected by illumination. However, the resolution of X and Y directions is low, so the traditional hand-eye calibration algorithm based on checkerboard is not suitable for hand-eye calibration of 3D cameras and robots. Therefore, a hand-eye calibration method for robot and 3D camera based on 3D calibration block is designed. Through the camera calibration of gray information, the use of template matching algorithm segmentation calibration block of each plane, calculate the multiple points in plane in pixel coordinates of the location and the pixel coordinates of depth information at the same time, the fitting of the calibration block multiple plane normal vector information, through multiple plane intersection location feature point out. Random sample consensus algorithm was used to eliminate the wrong feature points, and according to the correct feature points, the transformation relationship between the point cloud data of the calibration block obtained by the robot under different poses was solved. The hand eye calibration results were solved based on the current coordinates of the robots, and the error evaluation model was established to analyze the results. Finally, the comparison between this method and the hand-eye calibration method using calibration plate was carried out by 6 degrees of freedom robot arm of the DENSON and Cognex EA-5000 camera. The results show that this method can complete the calibration more quickly and accurately.