手眼标定算法Tsai-Lenz代码实现(Python、C++、Matlab)

手眼标定算法Tsai-Lenz代码实现(Python、C++、Matlab)上一节介绍了手眼标定算法Tsai的原理,这一节介绍算法的代码实现,分别有Python、C++、Matlab版本的算法实现方式。该算法适用于将相机装在手抓上和将相机装在外部两种情况论文已经传到git上,地址:https://gitee.com/ohhuo/handeye-tsai如果你要进行手眼标定,可以参考我的其他文章:手眼标定-基础使用手眼标定-JAKA机械臂手眼标定-AUBO机械臂手眼标定-Aruco使用与相机标定手眼标定-注意事项Python版本使用前需要安装库:pip3

大家好,又见面了,我是你们的朋友全栈君。

你好,我是小智。

上一节介绍了手眼标定算法Tsai的原理,这一节介绍算法的代码实现,分别有Python、C++、Matlab版本的算法实现方式。

  • 该算法适用于将相机装在手抓上和将相机装在外部两种情况
  • 论文已经传到git上,地址:https://gitee.com/ohhuo/handeye-tsai

如果你要进行手眼标定,可以参考我的其他文章:

如果上述程序使用过程中遇到问题,可以参考:

如果你对手眼标定原理感兴趣,可以参考以下文章:

Python版本

使用前需要安装库:

pip3 install transforms3d
pip3 install numpy
#!/usr/bin/env python
# coding: utf-8
import transforms3d as tfs
import numpy as np
import math
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]
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]
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]))

运行结果:

python3 tsai.py                             
[[-0.01522186 -0.99983174 -0.01023609 -0.02079774]
[ 0.99976822 -0.01506342 -0.01538198  0.00889827]
[ 0.0152252  -0.01046786  0.99982929  0.08324514]
[ 0.          0.          0.          1.        ]]

C++版本:

//Reference:
//R. Y. Tsai and R. K. Lenz, "A new technique for fully autonomous and efficient 3D robotics hand/eye calibration."
//In IEEE Transactions on Robotics and Automation, vol. 5, no. 3, pp. 345-358, June 1989.
//C++ code converted from Zoran Lazarevic's Matlab code:
//http://lazax.com/www.cs.columbia.edu/~laza/html/Stewart/matlab/handEye.m
static void calibrateHandEyeTsai(const std::vector<Mat>& Hg, const std::vector<Mat>& Hc,Mat& R_cam2gripper, Mat& t_cam2gripper)
{ 

//Number of unique camera position pairs
int K = static_cast<int>((Hg.size()*Hg.size() - Hg.size()) / 2.0);
//Will store: skew(Pgij+Pcij)
Mat A(3*K, 3, CV_64FC1);
//Will store: Pcij - Pgij
Mat B(3*K, 1, CV_64FC1);
std::vector<Mat> vec_Hgij, vec_Hcij;
vec_Hgij.reserve(static_cast<size_t>(K));
vec_Hcij.reserve(static_cast<size_t>(K));
int idx = 0;
for (size_t i = 0; i < Hg.size(); i++)
{ 

for (size_t j = i+1; j < Hg.size(); j++, idx++)
{ 

//Defines coordinate transformation from Gi to Gj
//Hgi is from Gi (gripper) to RW (robot base)
//Hgj is from Gj (gripper) to RW (robot base)
Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; //eq 6
vec_Hgij.push_back(Hgij);
//Rotation axis for Rgij which is the 3D rotation from gripper coordinate frame Gi to Gj
Mat Pgij = 2*rot2quatMinimal(Hgij);
//Defines coordinate transformation from Ci to Cj
//Hci is from CW (calibration target) to Ci (camera)
//Hcj is from CW (calibration target) to Cj (camera)
Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); //eq 7
vec_Hcij.push_back(Hcij);
//Rotation axis for Rcij
Mat Pcij = 2*rot2quatMinimal(Hcij);
//Left-hand side: skew(Pgij+Pcij)
skew(Pgij+Pcij).copyTo(A(Rect(0, idx*3, 3, 3)));
//Right-hand side: Pcij - Pgij
Mat diff = Pcij - Pgij;
diff.copyTo(B(Rect(0, idx*3, 1, 3)));
}
}
Mat Pcg_;
//Rotation from camera to gripper is obtained from the set of equations:
// skew(Pgij+Pcij) * Pcg_ = Pcij - Pgij (eq 12)
solve(A, B, Pcg_, DECOMP_SVD);
Mat Pcg_norm = Pcg_.t() * Pcg_;
//Obtained non-unit quaternion is scaled back to unit value that
//designates camera-gripper rotation
Mat Pcg = 2 * Pcg_ / sqrt(1 + Pcg_norm.at<double>(0,0)); //eq 14
Mat Rcg = quatMinimal2rot(Pcg/2.0);
idx = 0;
for (size_t i = 0; i < Hg.size(); i++)
{ 

for (size_t j = i+1; j < Hg.size(); j++, idx++)
{ 

//Defines coordinate transformation from Gi to Gj
//Hgi is from Gi (gripper) to RW (robot base)
//Hgj is from Gj (gripper) to RW (robot base)
Mat Hgij = vec_Hgij[static_cast<size_t>(idx)];
//Defines coordinate transformation from Ci to Cj
//Hci is from CW (calibration target) to Ci (camera)
//Hcj is from CW (calibration target) to Cj (camera)
Mat Hcij = vec_Hcij[static_cast<size_t>(idx)];
//Left-hand side: (Rgij - I)
Mat diff = Hgij(Rect(0,0,3,3)) - Mat::eye(3,3,CV_64FC1);
diff.copyTo(A(Rect(0, idx*3, 3, 3)));
//Right-hand side: Rcg*Tcij - Tgij
diff = Rcg*Hcij(Rect(3, 0, 1, 3)) - Hgij(Rect(3, 0, 1, 3));
diff.copyTo(B(Rect(0, idx*3, 1, 3)));
}
}
Mat Tcg;
//Translation from camera to gripper is obtained from the set of equations:
// (Rgij - I) * Tcg = Rcg*Tcij - Tgij (eq 15)
solve(A, B, Tcg, DECOMP_SVD);
R_cam2gripper = Rcg;
t_cam2gripper = Tcg;
}

C++版本食用方法:

终端指令

git clone https://gitee.com/ohhuo/handeye-tsai.git   
cd handeye-tsai/cpp     
mkdir build   
cd build
cmake ..   
make
./opencv_example 

示例:

sangxin@sangxin-ubu~ git clone https://gitee.com/ohhuo/handeye-tsai.git      
正克隆到 'handeye-tsai'...
remote: Enumerating objects: 60, done.
remote: Counting objects: 100% (60/60), done.
remote: Compressing objects: 100% (57/57), done.
remote: Total 60 (delta 9), reused 0 (delta 0), pack-reused 0
展开对象中: 100% (60/60), 完成.
sangxin@sangxin-ubu~ cd handeye-tsai/cpp                                                                                                                          
sangxin@sangxin-ubu~ mkdir build   
sangxin@sangxin-ubu~ cd build
sangxin@sangxin-ubu~ cmake ..        
-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identification is GNU 7.5.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found OpenCV: /usr/local (found version "4.5.1") 
-- OpenCV library status:
--     config: /usr/local/lib/cmake/opencv4
--     version: 4.5.1
--     libraries: opencv_calib3d;opencv_core;opencv_dnn;opencv_features2d;opencv_flann;opencv_gapi;opencv_highgui;opencv_imgcodecs;opencv_imgproc;opencv_ml;opencv_objdetect;opencv_photo;opencv_stitching;opencv_video;opencv_videoio
--     include path: /usr/local/include/opencv4
-- Configuring done
-- Generating done
-- Build files have been written to: /home/sangxin/code/ramp/other/handeye-tsai/cpp/build
sangxin@sangxin-ubu~ make     
Scanning dependencies of target opencv_example
[ 33%] Building CXX object CMakeFiles/opencv_example.dir/example.cpp.o
[ 66%] Building CXX object CMakeFiles/opencv_example.dir/calibration_handeye.cpp.o
[100%] Linking CXX executable opencv_example
[100%] Built target opencv_example
sangxin@sangxin-ubu~ ./opencv_example  
Hand eye calibration
[0.02534592279128711, -0.999507800830298, -0.01848621857599331, 0.03902588103574497;
0.99953544041497, 0.02502485833258339, 0.01739712102291752, 0.002933439485668206;
-0.01692594317342544, -0.01891857671220042, 0.9996777480282706, -0.01033683416650518;
0, 0, 0, 1]
Homo_cam2gripper 是否包含旋转矩阵:1

Matlab版本:

% handEye - performs hand/eye calibration
% 
%     gHc = handEye(bHg, wHc)
% 
%     bHg - pose of gripper relative to the robot base..
%           (Gripper center is at: g0 = Hbg * [0;0;0;1] )
%           Matrix dimensions are 4x4xM, where M is ..
%           .. number of camera positions. 
%           Algorithm gives a non-singular solution when ..
%           .. at least 3 positions are given
%           Hbg(:,:,i) is i-th homogeneous transformation matrix
%     wHc - pose of camera relative to the world ..      
%           (relative to the calibration block)
%           Dimension: size(Hwc) = size(Hbg)
%     gHc - 4x4 homogeneous transformation from gripper to camera      
%           , that is the camera position relative to the gripper.
%           Focal point of the camera is positioned, ..
%           .. relative to the gripper, at
%                 f = gHc*[0;0;0;1];
%           
% References: R.Tsai, R.K.Lenz "A new Technique for Fully Autonomous 
%           and Efficient 3D Robotics Hand/Eye calibration", IEEE 
%           trans. on robotics and Automaion, Vol.5, No.3, June 1989
%
% Notation: wHc - pose of camera frame (c) in the world (w) coordinate system
%                 .. If a point coordinates in camera frame (cP) are known
%                 ..     wP = wHc * cP
%                 .. we get the point coordinates (wP) in world coord.sys.
%                 .. Also refered to as transformation from camera to world
%
function gHc = handEye(bHg, wHc)
M = size(bHg,3);
K = (M*M-M)/2;               % Number of unique camera position pairs
A = zeros(3*K,3);            % will store: skew(Pgij+Pcij)
B = zeros(3*K,1);            % will store: Pcij - Pgij
k = 0;
% Now convert from wHc notation to Hc notation used in Tsai paper.
Hg = bHg;
% Hc = cHw = inv(wHc); We do it in a loop because wHc is given, not cHw
Hc = zeros(4,4,M); for i = 1:M, Hc(:,:,i) = inv(wHc(:,:,i)); end;
for i = 1:M,
for j = i+1:M;
Hgij = inv(Hg(:,:,j))*Hg(:,:,i);    % Transformation from i-th to j-th gripper pose
Pgij = 2*rot2quat(Hgij);            % ... and the corresponding quaternion
Hcij = Hc(:,:,j)*inv(Hc(:,:,i));    % Transformation from i-th to j-th camera pose
Pcij = 2*rot2quat(Hcij);            % ... and the corresponding quaternion
k = k+1;                            % Form linear system of equations
A((3*k-3)+(1:3), 1:3) = skew(Pgij+Pcij); % left-hand side
B((3*k-3)+(1:3))      = Pcij - Pgij;     % right-hand side
end;
end;
% Rotation from camera to gripper is obtained from the set of equations:
%    skew(Pgij+Pcij) * Pcg_ = Pcij - Pgij
% Gripper with camera is first moved to M different poses, then the gripper
% .. and camera poses are obtained for all poses. The above equation uses
% .. invariances present between each pair of i-th and j-th pose.
Pcg_ = A \ B;                % Solve the equation A*Pcg_ = B
% Obtained non-unit quaternin is scaled back to unit value that
% .. designates camera-gripper rotation
Pcg = 2 * Pcg_ / sqrt(1 + Pcg_'*Pcg_);
Rcg = quat2rot(Pcg/2);         % Rotation matrix
% Calculate translational component
k = 0;
for i = 1:M,
for j = i+1:M;
Hgij = inv(Hg(:,:,j))*Hg(:,:,i);    % Transformation from i-th to j-th gripper pose
Hcij = Hc(:,:,j)*inv(Hc(:,:,i));    % Transformation from i-th to j-th camera pose
k = k+1;                            % Form linear system of equations
A((3*k-3)+(1:3), 1:3) = Hgij(1:3,1:3)-eye(3); % left-hand side
B((3*k-3)+(1:3))      = Rcg(1:3,1:3)*Hcij(1:3,4) - Hgij(1:3,4);     % right-hand side
end;
end;
Tcg = A \ B;
gHc = transl(Tcg) * Rcg;	% incorporate translation with rotation
return

如果有错误的地方,还请各回指出,当第一时间改正~

作者介绍:

我是小智,机器人领域资深玩家,现深圳某独脚兽机器人算法工程师一枚

初中学习编程,高中开始学习机器人,大学期间打机器人相关比赛实现月入2W+(比赛奖金)

目前在输出机器人学习指南、论文注解、工作经验,欢迎大家关注小智,一起交流技术,学习机器人
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