摘要
目前用于单目视觉测量所采用的ArUco标定方法精度较低,无法满足高精度的定位和测距应用。提出了一种基于单目视觉的机器人定位算法,在原始标记周围使用额外的矩形图案进行增强,使其形成一个棋盘格的形状,并引入亚像素角点计算方法,精准定位标记角点坐标信息。最后,通过使用最小二乘法进行拟合,使用PnP算法计算相机相对于标记的空间位置信息,有效提高姿态估计和测距精度。测试结果表明,所提定位方法将深度方向提高了5 mm,误差仅为2 mm左右,相较现有的方法深度估计误差减小了66.7%,水平方向误差仅为1 mm。在复杂环境以及在部分遮挡的ArUco标记也能准确无误的识别。
The ArUco calibration method currently used for monocular vision measurement has low accuracy and cannot meet the applications of high-precision positioning and ranging.A robot positioning algorithm based on monocular vision is proposed.Additional rectangular patterns are used to enhance the original mark to form a checkerboard shape.A sub-pixel corner point calculation method is introduced to accurately locate the coordinate information of the corner point of the mark.Finally,by using the least squares method for fitting,the PnP algorithm is used to calculate the spatial position information of the camera relative to the marker,which effectively improves the accuracy of attitude estimation and ranging.Test results show that the proposed positioning method increases the depth direction by 5 mm,and the error is only 2 mm.Compared with the existing method,the depth estimation error is reduced by 66.7%,and the horizontal direction error is only 1 mm.ArUco markers can be accurately identified in complex environments and partially obscured areas.
作者
张云凡
江励
徐俊佳
汤健华
Zhang Yunfan;Jiang li;Xu Junjia;Tang Jianhua(School of Intelligent Manufacturing of Wuyi Univerity,Jiangmen,Guangdong 529000,China)
出处
《机电工程技术》
2024年第5期159-161,166,共4页
Mechanical & Electrical Engineering Technology
基金
区域联合基金项目(2019A1515110258)
国家自然科学基金青年基金项目(51905384)。
