基于双目视觉的机器人位姿标定技术研究

J4 ›› 2015, Vol. 14 ›› Issue (6): 33-36.

基于双目视觉的机器人位姿标定技术研究

1.中国地质大学自动化学院，武汉430074；2.安徽国防科技职业学院机电工程系，安徽六安237001

收稿日期:2015-03-06 出版日期:2015-06-15 发布日期:2015-06-15
作者简介:吴年祥，实验师，主要从事机器人智能检测与控制技术及实验实训管理研究.
基金资助:
安徽省教育厅自然科学研究基金资助项目（KJ2013B045）；安徽国防科技职业学院骨干教师项目（gfggjs201211）

Study on the Calibration of Robot Position and Posture Based on Binocular Vision Technology

1.School of Automation, China University of Geosciences,Wuhan 430074, China;2.Department of Mechanical and Electrical
Engineering,Anhui National Defense Vocational College, Liu'an, Anhui 237001, China

Received:2015-03-06 Online:2015-06-15 Published:2015-06-15

摘要/Abstract

摘要：

针对机器人位姿的测量，建立双目立体视觉传感器三坐标的数学模型。匹配相关的特征点，采用最小二乘法得到空间
特征点的三维坐标。利用外部位姿线性方程的建立与求解确立机器人基坐标与工件坐标的矩阵关系。通过靶标法来确定机
器人在某一位姿时工件坐标系和视觉传感器坐标系之间的齐次坐标变换矩阵的关系。实验结果显示，在没有噪声因素情况
下，利用线性方法求解机器人外部位姿是可行的，能够满足系统精度要求。

关键词: 机器人, 数学模型, 视觉传感器, 靶标法, 位姿标定

Abstract:

In this paper, the CMM mathematical model of binocular stereo visual sensor has been proposed for the measurements of
the robot position and posture. Matching the related feature points, three-dimensional coordinates of feature points is obtained by using
the least squares method. This study establishes the matrix relationship between the robot base coordinate and the workpiece
coordinate utilizing establishment and solution of the linear equations of external position and posture. This paper confirms the
homogeneous coordinate transformation matrix relationship between the workpiece and the vision sensor coordination when the robot is
in any position and posture through the target method. The experimental results show that the linear method for solving robot external
posture is feasible without the impact of noise, and it can meet the accuracy requirements.

Key words: robot, mathematical model, visual sensor, target method, calibration of pose

中图分类号:

TP242

吴年祥，殷蔚民，邹华东. 基于双目视觉的机器人位姿标定技术研究[J]. J4, 2015, 14(6): 33-36.

Wu Nianxiang, Yin Weimin, Zou Huadong. Study on the Calibration of Robot Position and Posture Based on Binocular Vision Technology[J]. J4, 2015, 14(6): 33-36.