摘要
基于红外LED特征的悬臂式掘进机截割头位姿视觉测量系统中,外参标定稳定性和红外LED光斑中心提取精确性对截割头位姿检测精度具有重要影响。现有外参标定方法需依靠经验将截割臂摆至正中位置(未知),标定结果存在较大波动。针对该问题,提出了一种基于多点固定的外参标定方法,该方法控制掘进机截割臂分别摆动到左上角、右上角、左下角、右下角4个已知极限位置并采集标靶图像,计算外参矩阵值,可有效提高外参标定稳定性。现有的灰度质心法采用像素的灰度值作为权重来计算光斑质心,精度只能到像素级,仅粗略满足实际应用需求。针对该问题,提出采用亚像素级边缘检测算法改进光斑中心提取方法:首先采用灰度质心法进行光斑中心粗提取,然后采用亚像素级边缘检测算法求出亚像素级边缘坐标,最后使用最小二乘法拟合光斑中心,实现光斑中心精确提取。实验结果表明:改进光斑中心提取方法将标靶LED灯间距最大测量误差从3.2 mm缩小为1 mm,提高了检测精度;基于多点固定的外参标定方法所获得的外参数矩阵比较稳定,平移矩阵中位移的最大变化幅度为15 mm,旋转矩阵中角度的最大变化幅度为1°;视觉测量系统改进前对截割头摆角的测量误差范围为[-1.2°,1.7°],改进后截割头水平摆角误差范围为[-0.5°,0.5°],垂直摆角误差范围为[-0.6°,0.6°],说明改进方法有效提高了截割头摆角的检测精度。
In the vision measurement system for cutting head position of cantilever roadheader based on infrared LED characteristics,the stability of external calibration and the accuracy of infrared LED spot center extraction have an important influence on the cutting head position detection accuracy.The existing external parameter calibration method relies on experience to swing the cutting arm to the center position(unknown),and the calibration results have large fluctuations.In order to solve the above problem,a multi-point fixed external parameter calibration method is proposed.This method controls the cutting arm of the roadheader to swing to the four known limit positions of upper left corner,upper right corner,lower left corner and lower right corner respectively,and collects the target images.The method calculates the value of the external parameter matrix,which can improve the stability of the external parameter calibration effectively.The existing gray-scale centroid method uses the grayscale value of the pixel as the weight to calculate the spot centroid.And the accuracy can only reach the pixel level,which only roughly meets the practical application requirements.In order to solve this problem,a sub-pixel edge detection algorithm is proposed to improve the spot center extraction method.Firstly,the gray-scale centroid method is used for coarse extraction of the spot center.Secondly,the sub-pixel level edge detection algorithm is used to find the sub-pixel level edge coordinates.Finally,the least squares method is used to fit the spot center to achieve accurate extraction of the spot center.The experimental results show that the improved spot center extraction method reduces the maximum measurement error of the target LED lamp spacing from 3.2 mm to 1 mm,which improves the detection accuracy.The external parameter matrix obtained by the multi-point fixed external parameter calibration method is relatively stable,the maximum variation of displacement in the translation matrix is 15 mm,and the maximum variation of angle in the rotation matrix is 1°.Before the improvement of the vision measurement system,the measurement error of the cutting head swing angle was within[-1.2°,1.7°].After the improvement,the error of the horizontal swing angle of the cutting head is within[-0.5°,0.5°]and the error of the vertical swing angle is within[-0.6°,0.6°].The results show that the improved method improves the detection accuracy of the cutting head swing angle effectively.
作者
张旭辉
谢楠
张超
杨文娟
张楷鑫
周创
ZHANG Xuhui;XIE Nan;ZHANG Chao;YANG Wenjuan;ZHANG Kaixin;ZHOU Chuang(College of Mechanical Engineering,Xi'an University of Science and Technology,Xi'an 710054,China;Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring,Xi'an 710054,China)
出处
《工矿自动化》
北大核心
2021年第7期1-7,共7页
Journal Of Mine Automation
基金
国家自然科学基金资助项目(51974228,51834006)
陕西省创新能力支撑计划项目(2018TD-032)
陕西省重点研发计划项目(2018ZDCXL-GY-06-04)。
关键词
悬臂式掘进机
截割头位姿检测
防爆工业相机
视觉测量
外参标定
光斑中心提取
cantilever roadheader
cutting head position detection
explosion-proof industrial camera
vision measurement
external parameter calibration
spot center extraction
