视觉slam14讲第二版ch7的pose_estimation_2d2d.cpp的代码注释（自己理解的）

按我自己的理解，注释了视觉slam14讲第二版ch7的pose_estimation_2d2d.cpp

#include 
#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;


void find_feature_matches(
        const Mat &img_1, const Mat &img_2,
        std::vector<KeyPoint> &keypoints_1,
        std::vector<KeyPoint> &keypoints_2,
        std::vector<DMatch> &matches);

void pose_estimation_2d2d(
        std::vector<KeyPoint> keypoints_1,
        std::vector<KeyPoint> keypoints_2,
        std::vector<DMatch> matches,
        Mat &R, Mat &t);

// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);

int main(int argc, char **argv){
    if (argc != 3) {
        cout << "usage: pose_estimation_2d2d img1 img2" << endl;
        return 1;
    }
    //-- 读取图像
    Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
    Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);
    assert(img_1.data && img_2.data && "Can not load images!");

    vector<KeyPoint> keypoints_1, keypoints_2;
    vector<DMatch> matches;
    find_feature_matches(img_1, img_2, keypoints_1, keypoints_2, matches);
    cout << "一共找到了" << matches.size() << "组匹配点" << endl;

    //-- 估计两张图像间运动
    Mat R, t;
    pose_estimation_2d2d(keypoints_1, keypoints_2, matches, R, t);

    //-- 验证E=t^R*scale
    Mat t_x =
            (Mat_<double>(3, 3) << 0, -t.at<double>(2, 0), t.at<double>(1, 0),
                    t.at<double>(2, 0), 0, -t.at<double>(0, 0),
                    -t.at<double>(1, 0), t.at<double>(0, 0), 0);

    cout << "t^R=" << endl << t_x * R << endl;

    //-- 验证对极约束
    Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
    for (DMatch m: matches) {
        Point2d pt1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
        Mat y1 = (Mat_<double>(3, 1) << pt1.x, pt1.y, 1);
        Point2d pt2 = pixel2cam(keypoints_2[m.trainIdx].pt, K);
        Mat y2 = (Mat_<double>(3, 1) << pt2.x, pt2.y, 1);
        Mat d = y2.t() * t_x * R * y1;
        cout << "epipolar constraint = " << d << endl;
    }



    return 0;
}

void find_feature_matches(const Mat &img_1, const Mat &img_2,
                          std::vector<KeyPoint> &keypoints_1,
                          std::vector<KeyPoint> &keypoints_2,
                          std::vector<DMatch> &matches) {
    //-- 初始化
    Mat descriptors_1, descriptors_2;
    // used in OpenCV3
    //创建一个检测器，检测关键点
    Ptr<FeatureDetector> detector = ORB::create();
    //创建描述子提取器
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr detector = FeatureDetector::create ( "ORB" );
    // Ptr descriptor = DescriptorExtractor::create ( "ORB" );
    //创建一个匹配器,汉明距离
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect(img_1, keypoints_1);
    detector->detect(img_2, keypoints_2);

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute(img_1, keypoints_1, descriptors_1);
    descriptor->compute(img_2, keypoints_2, descriptors_2);

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
    vector<DMatch> match;
    //BFMatcher matcher ( NORM_HAMMING );
    matcher->match(descriptors_1, descriptors_2, match);

    //-- 第四步:匹配点对筛选
    double min_dist = 10000, max_dist = 0;

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for (int i = 0; i < descriptors_1.rows; i++) {
        //match是上面描述子匹配后的东西，有四个操作，.distance是看两个点的汉明距离
        double dist = match[i].distance;
        if (dist < min_dist) min_dist = dist;
        if (dist > max_dist) max_dist = dist;
    }

    printf("-- Max dist : %f \n", max_dist);
    printf("-- Min dist : %f \n", min_dist);

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    for (int i = 0; i < descriptors_1.rows; i++) {
        if (match[i].distance <= max(2 * min_dist, 30.0)) {
            matches.push_back(match[i]);
        }
    }
}

Point2d pixel2cam(const Point2d &p, const Mat &K) {
    return Point2d
            (
                    (p.x - K.at<double>(0, 2)) / K.at<double>(0, 0),
                    (p.y - K.at<double>(1, 2)) / K.at<double>(1, 1)
            );
}

void pose_estimation_2d2d(std::vector<KeyPoint> keypoints_1,
                          std::vector<KeyPoint> keypoints_2,
                          std::vector<DMatch> matches,
                          Mat &R, Mat &t) {
    // 相机内参,TUM Freiburg2
    Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);

    //-- 把匹配点转换为vector的形式
    //point2f我觉得是（x，y）坐标
    vector<Point2f> points1;
    vector<Point2f> points2;
    //这里[matches[i].queryIdx]是求索引，前面不是说了match.会出现四个参数，前面出现的是distance，这里是queryIdx。.pt是关键点的坐标
    for (int i = 0; i < (int) matches.size(); i++) {
        points1.push_back(keypoints_1[matches[i].queryIdx].pt);
        points2.push_back(keypoints_2[matches[i].trainIdx].pt);
    }

    //-- 计算基础矩阵
    //这就是书上说的八点法，求出基础矩阵
    Mat fundamental_matrix;
    fundamental_matrix = findFundamentalMat(points1, points2, CV_FM_8POINT);
    cout << "fundamental_matrix is " << endl << fundamental_matrix << endl;

    //-- 计算本质矩阵
    Point2d principal_point(325.1, 249.7);  //相机光心, TUM dataset标定值
    double focal_length = 521;      //相机焦距, TUM dataset标定值
    Mat essential_matrix;
    essential_matrix = findEssentialMat(points1, points2, focal_length, principal_point);
    cout << "essential_matrix is " << endl << essential_matrix << endl;

    //-- 计算单应矩阵
    //-- 但是本例中场景不是平面，单应矩阵意义不大
    Mat homography_matrix;
    homography_matrix = findHomography(points1, points2, RANSAC, 3);
    cout << "homography_matrix is " << endl << homography_matrix << endl;

    //-- 从本质矩阵中恢复旋转和平移信息.
    // 此函数仅在Opencv3中提供
    recoverPose(essential_matrix, points1, points2, R, t, focal_length, principal_point);
    cout << "R is " << endl << R << endl;
    cout << "t is " << endl << t << endl;

}