c++基于opencv对机械臂和相机手眼标定的代码

下面是基于OpenCV实现机械臂和相机手眼标定的C++代码示例：

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/aruco.hpp>

using namespace std;
using namespace cv;

int main()
{
    // 读取相机图像
    Mat cameraImage = imread("camera.jpg");

    // 读取机械臂姿态数据
    vector<Mat> robotPoses;
    for (int i = 1; i <= 10; i++) {
        string filename = "robot" + to_string(i) + ".txt";
        Mat pose = Mat::zeros(4, 4, CV_64F);
        ifstream fin(filename);
        for (int j = 0; j < 4; j++) {
            for (int k = 0; k < 4; k++) {
                fin >> pose.at<double>(j, k);
            }
        }
        robotPoses.push_back(pose);
    }

    // 读取相机姿态数据
    vector<Mat> cameraPoses;
    for (int i = 0; i < 5; i++) {
        string filename = "camera" + to_string(i) + ".txt";
        Mat pose = Mat::zeros(4, 4, CV_64F);
        ifstream fin(filename);
        for (int j = 0; j < 4; j++) {
            for (int k = 0; k < 4; k++) {
                fin >> pose.at<double>(j, k);
            }
        }
        cameraPoses.push_back(pose);
    }

    // 棋盘格参数
    int boardWidth = 9;  // 棋盘格宽度
    int boardHeight = 6;  // 棋盘格高度
    float squareSize = 25.0f;  // 棋盘格大小（毫米）

    // 棋盘格角点坐标
    vector<vector<Point3f>> objectPoints;
    for (int i = 0; i < robotPoses.size(); i++) {
        vector<Point3f> obj;
        for (int j = 0; j < boardHeight; j++) {
            for (int k = 0; k < boardWidth; k++) {
                obj.push_back(Point3f(j * squareSize, k * squareSize, 0));
            }
        }
        objectPoints.push_back(obj);
    }

    // 计算机械臂末端执行器和相机之间的位姿矩阵
    vector<Mat> robotToCameraPoses;
    for (int i = 0; i < robotPoses.size(); i++) {
        Mat robotPose = robotPoses[i];
        Mat cameraPose = cameraPoses[i];
        Mat robotToCameraPose = cameraPose.inv() * robotPose;
        robotToCameraPoses.push_back(robotToCameraPose);
    }

    // 相机标定
    vector<vector<Point2f>> imagePoints;
    vector<int> markerIds;
    vector<vector<Point2f>> markerCorners;
    Ptr<aruco::Dictionary> dictionary = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);
    aruco::detectMarkers(cameraImage, dictionary, markerCorners, markerIds);
    aruco::drawDetectedMarkers(cameraImage, markerCorners, markerIds);
    vector<Vec3d> rvecs, tvecs;
    aruco::estimatePoseSingleMarkers(markerCorners, squareSize, cameraMatrix, distCoeffs, rvecs, tvecs);
    for (int i = 0; i < markerIds.size(); i++) {
        vector<Point2f> corners = markerCorners[i];
        imagePoints.push_back(corners);
    }

    // 手眼标定
    Mat R, T;
    solveHandEye(objectPoints, imagePoints, robotToCameraPoses, R, T);

    // 输出结果
    cout << "Hand-eye calibration matrix:" << endl << R << endl << T << endl;

    return 0;
}

在这个示例中，我们首先读取了机械臂和相机的姿态数据，然后定义了棋盘格的参数和角点坐标，并通过`aruco::detectMarkers`和`aruco::estimatePoseSingleMarkers`函数检测和计算出相机图像中的标记物的位姿矩阵。接着，我们计算机械臂末端执行器和相机之间的位姿矩阵，并使用`solveHandEye`函数对机械臂和相机之间的转换矩阵进行计算，并输出结果。

需要注意的是，这个示例仅供参考，具体的实现还需要根据实际情况进行调整和优化，例如相机标定的方法和标记物的选择等。