两周内看了好多博客，博客上的代码甚至github上的代码都试过了一遍，各种语言matlab、c++、python，了解到了许多做手眼标定的平台——halcon、ros（这俩还需要从头开始学，时间不太够用），最后看到了鱼香ros的博客，参考了一下并总结完整，附链接
此博客仅记录学习过程总结思路，可以借鉴，有问题可以指出并联系我
基于ROS的机械臂手眼标定-基础使用_鱼香ros手眼标定_鱼香ROS的博客-CSDN博客
目录
手眼标定原理
获得手眼矩阵X
验证准确性
手眼标定原理
眼在手上，眼在手上的目的是求出末端到相机的变换矩阵X，也成为了手眼矩阵
由图可知，
标定板在机械臂坐标系下的位姿=标定板在相机坐标系下的位姿—>相机在末端坐标系下的位姿—>末端在机械臂基坐标系下的位姿
base to end 可以通过机械臂运动学得到
end to camera 是代求的X
camera to object 是通过相机拍下标定板照片得到
只要有两组数据，就可以列恒等式
除了X外，剩下的矩阵都可通过以上手法求得，通过数学方法移项后就是我们常说的AX=XB
接下来的目的就是求得X
获得手眼矩阵X
eyeinhandcalibrate.py
def get_matrix_eular_radu(x, y, z, rx, ry, rz):
rmat = tfs.euler.euler2mat(math.radians(rx), math.radians(ry), math.radians(rz))
rmat = tfs.affines.compose(np.squeeze(np.asarray((x, y, z))), rmat, [1, 1, 1])
return rmat
def skew(v):
return np.array([[0, -v[2], v[1]],
[v[2], 0, -v[0]],
[-v[1], v[0], 0]])
def rot2quat_minimal(m):
quat = tfs.quaternions.mat2quat(m[0:3, 0:3])
return quat[1:]
def quatMinimal2rot(q):
p = np.dot(q.T, q)
w = np.sqrt(np.subtract(1, p[0][0]))
return tfs.quaternions.quat2mat([w, q[0], q[1], q[2]])
# hand = [1.1988093940033604, -0.42405585264804424, 0.18828251788562061, 151.3390418721659, -18.612399542280507,
# 153.05074895025035,
# 1.1684831621733476, -0.183273375514656, 0.12744868246620855, -161.57083804238462, 9.07159838346732,
# 89.1641128844487,
# 1.1508343174145468, -0.22694301453461405, 0.26625166858469146, 177.8815855486261, 0.8991159570568988,
# 77.67286224959672]
hand = [
# -0.05448,-0.15018,0.06552,89.61059916,-2.119943842,-1.031324031,
# -0.10149,-0.23025,0.04023,96.7725716,6.187944187,5.328507495,
# -0.10114,-0.2207,0.04853,97.00175472,5.729577951,1.375098708 毫米单位
-54.48, -150.18, 65.52, 89.61059916, -2.119943842, -1.031324031,
-101.49,-230.25, 40.23, 96.7725716, 6.187944187, 5.328507495,
-101.14,-220.7 , 48.53, 97.00175472, 5.729577951, 1.375098708
]
# camera = [-0.16249272227287292, -0.047310635447502136, 0.4077761471271515, -56.98037030812389, -6.16739631361851,
# -115.84333735802369,
# 0.03955405578017235, -0.013497642241418362, 0.33975949883461, -100.87129330834215, -17.192685528625265,
# -173.07354634882094,
# -0.08517949283123016, 0.00957852229475975, 0.46546608209609985, -90.85270962096058, 0.9315977976503153,
# 175.2059707654342]
camera = [
# -0.0794887,-0.0812433,0.0246,0.0008,0.0033,0.0182,
# -0.078034,-0.0879632,0.4881494,-0.1085,0.0925,-0.1569,
# -0.1086702,-0.0881681,0.4240367,-0.1052,0.1251,-0.1124,
-79.4887, -81.2433, 24.6, 0.0008, 0.0033, 0.0182,
-78.034, -87.9632, 488.1494, -0.1085, 0.0925, -0.1569,
-108.6702, -88.1681, 424.0367, -0.1052, 0.1251, -0.1124,
]
Hgs, Hcs = [], []
for i in range(0, len(hand), 6):
Hgs.append(get_matrix_eular_radu(hand[i], hand[i + 1], hand[i + 2], hand[i + 3], hand[i + 4], hand[i + 5],))
Hcs.append(
get_matrix_eular_radu(camera[i], camera[i + 1], camera[i + 2], camera[i + 3], camera[i + 4], camera[i + 5]))
Hgijs = []
Hcijs = []
A = []
B = []
size = 0
for i in range(len(Hgs)):
for j in range(i + 1, len(Hgs)):
size += 1
Hgij = np.dot(np.linalg.inv(Hgs[j]), Hgs[i])
Hgijs.append(Hgij)
Pgij = np.dot(2, rot2quat_minimal(Hgij))
Hcij = np.dot(Hcs[j], np.linalg.inv(Hcs[i]))
Hcijs.append(Hcij)
Pcij = np.dot(2, rot2quat_minimal(Hcij))
A.append(skew(np.add(Pgij, Pcij)))
B.append(np.subtract(Pcij, Pgij))
MA = np.asarray(A).reshape(size * 3, 3)
MB = np.asarray(B).reshape(size * 3, 1)
Pcg_ = np.dot(np.linalg.pinv(MA), MB)
pcg_norm = np.dot(np.conjugate(Pcg_).T, Pcg_)
Pcg = np.sqrt(np.add(1, np.dot(Pcg_.T, Pcg_)))
Pcg = np.dot(np.dot(2, Pcg_), np.linalg.inv(Pcg))
Rcg = quatMinimal2rot(np.divide(Pcg, 2)).reshape(3, 3)
A = []
B = []
id = 0
for i in range(len(Hgs)):
for j in range(i + 1, len(Hgs)):
Hgij = Hgijs[id]
Hcij = Hcijs[id]
A.append(np.subtract(Hgij[0:3, 0:3], np.eye(3, 3)))
B.append(np.subtract(np.dot(Rcg, Hcij[0:3, 3:4]), Hgij[0:3, 3:4]))
id += 1
MA = np.asarray(A).reshape(size * 3, 3)
MB = np.asarray(B).reshape(size * 3, 1)
Tcg = np.dot(np.linalg.pinv(MA), MB).reshape(3, )
print(tfs.affines.compose(Tcg, np.squeeze(Rcg), [1, 1, 1]))
其中，hand为基坐标系下夹爪的位姿，一般从示教器上获取，我用的是ur5机械臂，注意单位mm和rad
三行为三组数据，hand=(x,y,z,rx,ry,rz)，同时应将弧度制转为角度制
camera后输入的为相机的外参（平移向量和旋转矩阵），相机中标定板的位姿
三行为三组数据，camera=(x,y,z,rx,ry,rz)，同时应将弧度制转为角度制
我是使用matlab工具包对相机外参进行标定的
注：得到的旋转向量应转为旋转矩阵，再转为欧拉角，Python、matlab代码见如下链接
手眼标定必备——旋转向量转换为旋转矩阵python——罗德里格斯公式Rodrigues_邸笠佘司的博客-CSDN博客
输出的即为手眼标定矩阵X
[[ 9.97623261e-01 -4.70586261e-02 5.03320417e-02 9.72987830e+02]
[ 4.65612713e-02 9.98854765e-01 1.10094003e-02 -1.27118401e+03]
[-5.07924869e-02 -8.63971002e-03 9.98671857e-01 -4.29111524e+02]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]验证准确性
根据手眼标定原理可知，
使用理论验证法，使用两组数据用等式将其联立，求得X文章来源：https://www.toymoban.com/news/detail-609491.html
clc;
%T1*X*T2 = T3*X*T4 = T5*X*T6
%T1为相机到标定板，T2为基坐标到末端
T1 = [ 0.9998 -0.0182 0.0033 -79.4887
0.0182 0.9998 -0.0008 -81.2433
-0.0033 0.0009 1.0000 24.6000
0 0 0 1.0000 ];
T2 = [ -0.2681 -0.4478 -0.8530 -54.4800
-0.3725 -0.7684 0.5205 -150.1800
-0.8885 0.4572 0.0392 65.5200
0 0 0 1.0000];
T3 = [ 0.9835 0.1556 0.0924 -78.0340
-0.1652 0.9803 0.1078 -87.9632
-0.0738 -0.1213 0.9899 488.1494
0 0 0 1.0000 ];
T4 = [ 0.5753 0.8124 -0.0951 -101.4900
0.6340 -0.5163 -0.5758 -230.2500
-0.5169 0.2709 -0.8120 40.2300
0 0 0 1.0000 ] ;
T5 = [ 0.9859 0.1113 0.1248 -108.6702
-0.1246 0.9867 0.1042 -88.1681
-0.1115 -0.1183 0.9867 424.0367
0 0 0 1.0000 ] ;
T6 = [ 0.1654 -0.8344 -0.5258 -101.1400
-0.9468 0.0149 -0.3215 -220.7000
0.2761 0.5510 -0.7875 48.5300
0 0 0 1.0000
];
%X = [[ 0.997623261 -0.0470586261 0.0503320417 972.987830]
% [ 0.0465612713 0.998854765 0.0110094003 -1271.18401]
% [-0.0507924869 -0.00863971002 0.998671857 -429.111524]
% [ 0 0 0 1]]
%ans1 = T1*X*T2
%ans2 = T3*X*T4
%ans3 = T5*X*T6
T1*X*T2==T3*X*T4
X结果如下文章来源地址https://www.toymoban.com/news/detail-609491.html
X =
1.0e+03 *
0.0010 -0.0000 0.0001 0.9730
0.0000 0.0010 0.0000 -1.2712
-0.0001 -0.0000 0.0010 -0.4291
0 0 0 0.0010到了这里，关于机械臂手眼标定realsense d435相机——眼在手上python、matlab的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
