视觉里程计：2D-2D 对极几何、3D-2D PnP、3D-3D ICP

参考链接：https://mp.weixin.qq.com/s/89IHjqnw-JJ1Ak_YjWdHvA

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
// #include "extra.h" // use this if in OpenCV2
 
using namespace std;
using namespace cv;
 
/****************************************************
 * 本程序演示了如何使用2D-2D的特征匹配估计相机运动
 * **************************************************/
 
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 != ) {
    cout << "usage: pose_estimation_2d2d img1 img2" << endl;
    return ;
  }
  //-- 读取图像
  Mat img_1 = imread(argv[], CV_LOAD_IMAGE_COLOR);
  Mat img_2 = imread(argv[], 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>(, ) << , -t.at<double>(, ), t.at<double>(, ),
      t.at<double>(, ), , -t.at<double>(, ),
      -t.at<double>(, ), t.at<double>(, ), );
 
  cout << "t^R=" << endl << t_x * R << endl;
 
  //-- 验证对极约束
  Mat K = (Mat_<double>(, ) << 520.9, , 325.1, , 521.0, 249.7, , , );
  for (DMatch m: matches) {
    Point2d pt1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
    Mat y1 = (Mat_<double>(, ) << pt1.x, pt1.y, );
    Point2d pt2 = pixel2cam(keypoints_2[m.trainIdx].pt, K);
    Mat y2 = (Mat_<double>(, ) << pt2.x, pt2.y, );
    Mat d = y2.t() * t_x * R * y1;
    cout << "epipolar constraint = " << d << endl;
  }
  return ;
}
 
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
  Ptr<FeatureDetector> detector = ORB::create();
  Ptr<DescriptorExtractor> descriptor = ORB::create();
  // use this if you are in OpenCV2
  // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
  // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
  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 = , max_dist = ;
 
  //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 = ; i < descriptors_1.rows; i++) {
    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 = ; i < descriptors_1.rows; i++) {
    if (match[i].distance <= max( * min_dist, 30.0)) {
      matches.push_back(match[i]);
    }
  }
}
 
Point2d pixel2cam(const Point2d &p, const Mat &K) {
  return Point2d
    (
      (p.x - K.at<double>(, )) / K.at<double>(, ),
      (p.y - K.at<double>(, )) / K.at<double>(, )
    );
}
 
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>(, ) << 520.9, , 325.1, , 521.0, 249.7, , , );
 
  //-- 把匹配点转换为vector<Point2f>的形式
  vector<Point2f> points1;
  vector<Point2f> points2;
 
  for (int i = ; 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
  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 = ;      //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, );
  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;
 
}

#include <iostream>
#include <opencv2/opencv.hpp>
// #include "extra.h" // used in opencv2
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(
  const std::vector<KeyPoint> &keypoints_1,
  const std::vector<KeyPoint> &keypoints_2,
  const std::vector<DMatch> &matches,
  Mat &R, Mat &t);
 
void triangulation(
  const vector<KeyPoint> &keypoint_1,
  const vector<KeyPoint> &keypoint_2,
  const std::vector<DMatch> &matches,
  const Mat &R, const Mat &t,
  vector<Point3d> &points
);
 
/// 作图用
inline cv::Scalar get_color(float depth) {
  float up_th = , low_th = , th_range = up_th - low_th;
  if (depth > up_th) depth = up_th;
  if (depth < low_th) depth = low_th;
  return cv::Scalar( * depth / th_range, ,  * ( - depth / th_range));
}
 
// 像素坐标转相机归一化坐标
Point2f pixel2cam(const Point2d &p, const Mat &K);
 
int main(int argc, char **argv) {
  if (argc != ) {
    cout << "usage: triangulation img1 img2" << endl;
    return ;
  }
  //-- 读取图像
  Mat img_1 = imread(argv[], CV_LOAD_IMAGE_COLOR);
  Mat img_2 = imread(argv[], CV_LOAD_IMAGE_COLOR);
 
  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);
 
  //-- 三角化
  vector<Point3d> points;
  triangulation(keypoints_1, keypoints_2, matches, R, t, points);
 
  //-- 验证三角化点与特征点的重投影关系
  Mat K = (Mat_<double>(, ) << 520.9, , 325.1, , 521.0, 249.7, , , );
  Mat img1_plot = img_1.clone();
  Mat img2_plot = img_2.clone();
  for (int i = ; i < matches.size(); i++) {
    // 第一个图
    float depth1 = points[i].z;
    cout << "depth: " << depth1 << endl;
    Point2d pt1_cam = pixel2cam(keypoints_1[matches[i].queryIdx].pt, K);//由匹配点的像素坐标得到相机坐标
    cv::circle(img1_plot, keypoints_1[matches[i].queryIdx].pt, , get_color(depth1), );//画出匹配点，颜色由深度决定
 
    // 第二个图
    Mat pt2_trans = R * (Mat_<double>(, ) << points[i].x, points[i].y, points[i].z) + t;
    float depth2 = pt2_trans.at<double>(, );
    cv::circle(img2_plot, keypoints_2[matches[i].trainIdx].pt, , get_color(depth2), );
  }
  cv::imshow("img 1", img1_plot);
  cv::imshow("img 2", img2_plot);
  cv::waitKey();
 
  return ;
}
 
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
  Ptr<FeatureDetector> detector = ORB::create();
  Ptr<DescriptorExtractor> descriptor = ORB::create();
  // use this if you are in OpenCV2
  // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
  // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
  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 = , max_dist = ;
 
  //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 = ; i < descriptors_1.rows; i++) {
    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 = ; i < descriptors_1.rows; i++) {
    if (match[i].distance <= max( * min_dist, 30.0)) {
      matches.push_back(match[i]);
    }
  }
}
 
void pose_estimation_2d2d(
  const std::vector<KeyPoint> &keypoints_1,
  const std::vector<KeyPoint> &keypoints_2,
  const std::vector<DMatch> &matches,
  Mat &R, Mat &t) {
  // 相机内参,TUM Freiburg2
  Mat K = (Mat_<double>(, ) << 520.9, , 325.1, , 521.0, 249.7, , , );
 
  //-- 把匹配点转换为vector<Point2f>的形式
  vector<Point2f> points1;
  vector<Point2f> points2;
 
  for (int i = ; i < (int) matches.size(); i++) {
    points1.push_back(keypoints_1[matches[i].queryIdx].pt);
    points2.push_back(keypoints_2[matches[i].trainIdx].pt);
  }
 
  //-- 计算本质矩阵
  Point2d principal_point(325.1, 249.7);        //相机主点, TUM dataset标定值
  int focal_length = ;            //相机焦距, TUM dataset标定值
  Mat essential_matrix;
  essential_matrix = findEssentialMat(points1, points2, focal_length, principal_point);
 
  //-- 从本质矩阵中恢复旋转和平移信息.
  recoverPose(essential_matrix, points1, points2, R, t, focal_length, principal_point);
}
 
void triangulation(
  const vector<KeyPoint> &keypoint_1,
  const vector<KeyPoint> &keypoint_2,
  const std::vector<DMatch> &matches,
  const Mat &R, const Mat &t,
  vector<Point3d> &points) {
  Mat T1 = (Mat_<float>(, ) <<
    , , , ,
    , , , ,
    , , , );
  Mat T2 = (Mat_<float>(, ) <<
    R.at<double>(, ), R.at<double>(, ), R.at<double>(, ), t.at<double>(, ),
    R.at<double>(, ), R.at<double>(, ), R.at<double>(, ), t.at<double>(, ),
    R.at<double>(, ), R.at<double>(, ), R.at<double>(, ), t.at<double>(, )
  );
 
  Mat K = (Mat_<double>(, ) << 520.9, , 325.1, , 521.0, 249.7, , , );
  vector<Point2f> pts_1, pts_2;
  for (DMatch m:matches) {
    // 将像素坐标转换至相机坐标
    pts_1.push_back(pixel2cam(keypoint_1[m.queryIdx].pt, K));
    pts_2.push_back(pixel2cam(keypoint_2[m.trainIdx].pt, K));
  }
 
  Mat pts_4d;
  cv::triangulatePoints(T1, T2, pts_1, pts_2, pts_4d);
 
  // 转换成非齐次坐标
  for (int i = ; i < pts_4d.cols; i++) {
    Mat x = pts_4d.col(i);
    x /= x.at<float>(, ); // 归一化
    Point3d p(
      x.at<float>(, ),
      x.at<float>(, ),
      x.at<float>(, )
    );
    points.push_back(p);
  }
}
 
Point2f pixel2cam(const Point2d &p, const Mat &K) {
  return Point2f
    (
      (p.x - K.at<double>(, )) / K.at<double>(, ),
      (p.y - K.at<double>(, )) / K.at<double>(, )
    );
}