用C++写一个连续法相对定向的算法

下面是一个简单的连续法相对定向的C++代码示例，仅供参考：

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

using namespace std;
using namespace cv;

// 特征点匹配和基础矩阵计算
bool matchFeatures(Mat img1, Mat img2, vector<Point2f>& pts1, vector<Point2f>& pts2, Mat& F)
{
    // 特征点提取和匹配
    vector<KeyPoint> kpts1, kpts2;
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");

    detector->detect(img1, kpts1);
    detector->detect(img2, kpts2);

    Mat desc1, desc2;
    descriptor->compute(img1, kpts1, desc1);
    descriptor->compute(img2, kpts2, desc2);

    vector<DMatch> matches;
    matcher->match(desc1, desc2, matches);

    // 筛选匹配点
    double min_dist = 10000, max_dist = 0;
    for (int i = 0; i < desc1.rows; ++i) {
        double dist = matches[i].distance;
        if (dist < min_dist) min_dist = dist;
        if (dist > max_dist) max_dist = dist;
    }

    pts1.clear();
    pts2.clear();
    for (int i = 0; i < desc1.rows; ++i) {
        if (matches[i].distance <= max(2 * min_dist, 30.0)) {
            pts1.push_back(kpts1[matches[i].queryIdx].pt);
            pts2.push_back(kpts2[matches[i].trainIdx].pt);
        }
    }

    // 计算基础矩阵
    F = findFundamentalMat(pts1, pts2, FM_RANSAC, 3, 0.99);

    return true;
}

// 相对定向计算
bool relativeOrientation(vector<Point2f> pts1, vector<Point2f> pts2, Mat K, Mat& R, Mat& t)
{
    Mat E = findEssentialMat(pts1, pts2, K, RANSAC, 0.999, 1.0);

    Mat R1, R2, t1, t2;
    int n = recoverPose(E, pts1, pts2, K, R1, t1);

    if (n == 0) return false;

    R = R1;
    t = t1;

    return true;
}

// 主函数
int main()
{
    // 读取影像
    Mat img1 = imread("img1.jpg");
    Mat img2 = imread("img2.jpg");

    // 相机内参矩阵
    Mat K = Mat::eye(3, 3, CV_64F);
    K.at<double>(0, 0) = 1000;
    K.at<double>(1, 1) = 1000;
    K.at<double>(0, 2) = img1.cols / 2;
    K.at<double>(1, 2) = img1.rows / 2;

    // 特征点匹配和基础矩阵计算
    vector<Point2f> pts1, pts2;
    Mat F;
    matchFeatures(img1, img2, pts1, pts2, F);

    // 相对定向计算
    Mat R, t;
    relativeOrientation(pts1, pts2, K, R, t);

    // 输出结果
    cout << "R = " << endl;
    cout << R << endl;
    cout << "t = " << endl;
    cout << t << endl;

    return 0;
}

此代码示例利用ORB算法提取特征点，并使用RANSAC算法进行基础矩阵估计和相对定向计算。在实际应用中，还需要考虑更多的细节问题，如特征点筛选、误差控制、相机标定等。