摘要
针对标定过程中工业机器人工具末端姿态测量精度低的问题,提出了一种基于改进最小二乘法的双目机器人工具末端姿态测量算法。为了减少噪声对拟合收敛性的影响,提高拟合的精度和速度,该算法融合"邻域加权平均法"的去噪原理,结合空间解析几何理论和最小二乘法,对双目相机测量得到的点云数据进行筛选和特征提取。利用拟合模型完成点云数据的轴线拟合,实现对末端姿态的自动测量。实验表明,该算法相比传统姿态标定算法的测量精度提高0.2°,保证了工业机器人的标定精度,在工业上具有较强的应用价值。
Aiming at the problem of low precision of attitude measurement in the calibration process,an improved least square method based attitude measurement algorithm for binocular robot tools was proposed. In order to reduce the influence of noise on the convergence of the fitting and improve the accuracy and speed of the fitting,this algorithm fuses the denoising principle of"neighborhood weighted average method"and combines the spatial analytic geometry theory and the least square method to screen and extract the features of the point cloud data measured by binocular camera. The fitting model is used to complete the axis fitting of point cloud data and realize the automatic measurement of the end attitude. The experiment shows that the accuracy of this algorithm is improved by 0.2° compared with the traditional attitude calibration algorithm,which ensures the calibration accuracy of industrial robots and has strong application value in industry.
作者
曹梦龙
靳利文
CAO Menglong;JIN Liwen(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao Shandong 266042,China)
出处
《传感技术学报》
CAS
CSCD
北大核心
2020年第12期1759-1765,共7页
Chinese Journal of Sensors and Actuators
基金
校企合作项目(20183702022958)。
关键词
机器视觉
姿态测量
最小二乘法
特征提取
点云数据
machine vision
attitude measurement
least-multiplication
feature extraction
point cloud data
