1.对极约束求解相机运动——R,t
步骤:
- 根据配对点的像素位置求出本质矩阵E(八点法、SVD奇异值分解)或者基础矩阵F(
x2T?Ex1?=p2T?Fp1?=0) 注意:
x1?,x2?为3d点分别在第一帧、第二帧相机归一化坐标系上的坐标;
p1?,p2?为像素坐标。
- 根据E/F求出R,t(
E=t/R,F=K?TEK?1),K是相机内参矩阵
第三种方法是求单应矩阵
注意findHomography() 函数形式和参数,详见参考资料
参考资料 :视觉slam学习之——ch7 视觉里程计
关键代码:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | 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<Point2f>的形式 vector<Point2f> points1; vector<Point2f> points2; 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 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; } |
注意recoverPose()函数原型和参数,详见参考资料。
实验结果
注意:编译前讲参考图片放到build文件下,终端输入
2.三角测量
- 基础知识点补充:
1.1 Mat::at
2. triangulatePoints()的参数形式详见参考资料。
相关代码:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 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>(3, 4) << 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0); //这块表示图1的相机作为参考,变换矩阵中没有相机旋转和平移 Mat T2 = (Mat_<float>(3, 4) << R.at<double>(0, 0), R.at<double>(0, 1), R.at<double>(0, 2), t.at<double>(0, 0), R.at<double>(1, 0), R.at<double>(1, 1), R.at<double>(1, 2), t.at<double>(1, 0), R.at<double>(2, 0), R.at<double>(2, 1), R.at<double>(2, 2), t.at<double>(2, 0) ); //T2中传入R,t,即图2相对图1这个参考位置的变换矩阵 Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1); //相机内参 vector<Point2f> pts_1, pts_2; //图1和图1上的2D图像像素点转换后的归一化相机坐标点,对应的Zc=1,所以只要计算Xc和Yc 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); //传入两个图像对应相机的变化矩阵,各自相机坐标系下归一化相机坐标,输出的3D坐标是齐次坐标,共四个维度,因此需要将前三个维度除以第四个维度以得到非齐次坐标xyz // 转换成非齐次坐标 for (int i = 0; i < pts_4d.cols; i++) { //遍历所有的点,列数表述点的数量 Mat x = pts_4d.col(i); //x为4x1维度 x /= x.at<float>(3, 0); // 归一化 Point3d p( x.at<float>(0, 0), x.at<float>(1, 0), x.at<float>(2, 0) ); points.push_back(p); //将图1测得的目标相对相机实际位置(Xc,Yc,Zc)存入points } } Point2f pixel2cam(const Point2d &p, const Mat &K) { return Point2f ( (p.x - K.at<double>(0, 2)) / K.at<double>(0, 0), (p.y - K.at<double>(1, 2)) / K.at<double>(1, 1) ); } |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | int main(int argc, char **argv) { if (argc != 3) { cout << "usage: triangulation img1 img2" << endl; return 1; } //-- 读取图像 Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR); //加载彩色图 Mat img_2 = imread(argv[2], 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 // 传入图1,2的特征点,刷选后的匹配结果,由本质矩阵恢复得到的R和t,输出图1中目标在其相机姿态中的3D实际相机坐标(Xc,Yc,Zc) 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>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1); //相机内参 Mat img1_plot = img_1.clone(); Mat img2_plot = img_2.clone(); for (int i = 0; i < matches.size(); i++) { //遍历刷选后的匹配点对 // 第一个图 float depth1 = points[i].z; //图1中第i个匹配的距离 cout << "depth: " << depth1 << endl; Point2d pt1_cam = pixel2cam(keypoints_1[matches[i].queryIdx].pt, K); // 像素坐标->归一化相机坐标 cv::circle(img1_plot, keypoints_1[matches[i].queryIdx].pt, 2, get_color(depth1), 2); //在图1中画出刷选后的匹配点(颜色与距离相关) // 第二个图 Mat pt2_trans = R * (Mat_<double>(3, 1) << points[i].x, points[i].y, points[i].z) + t; float depth2 = pt2_trans.at<double>(2, 0); cv::circle(img2_plot, keypoints_2[matches[i].trainIdx].pt, 2, get_color(depth2), 2); } cv::imshow("img 1", img1_plot); cv::imshow("img 2", img2_plot); cv::waitKey(); return 0; } |
实验结果: