Question

我正在尝试根据场景中找到的基准标记确定相机姿势。

基准：http://tinypic.com/view.php?pic=4r6k3q&s=8#.VNLnWTVVK1E

当前流程：

使用SIFT进行特征检测
使用SIFT进行描述符提取
使用FLANN进行匹配
使用CV_RANSAC
确定基准的角落
使用perspectiveTransform（）
在角落周围画线（即证明它在场景中找到了基准
运行相机校准
加载校准结果（cameraMatrix＆amp; distortionCoefficients）

现在我想弄清楚相机的姿势。我试图使用：

void solvePnP（const Mat＆amp; objectPoints，const Mat＆amp; imagePoints，const Mat＆amp; cameraMatrix，const Mat＆amp; distCoeffs，垫＆安培; rvec，Mat＆amp; tvec，bool useExtrinsicGuess = false）

其中：

obectPoints是基准角落
imagePoints是场景中的基准角落
cameraMatrix来自校准
distCoeffs来自校准
rvec和tvec应该从这个函数返回给我

然而，当我运行它时，我收到核心转储错误，所以我不确定我做错了什么。

我还没有找到关于solvePNP（）的非常好的文档 - 我是否误解了函数或输入参数？

感谢您的帮助

更新这是我的过程：

OrbFeatureDetector detector; //Orb seems more accurate than SIFT
vector<KeyPoint> keypoints1, keypoints2; 

detector.detect(marker_im, keypoints1);
detector.detect(scene_im, keypoints2);

Mat display_marker_im, display_scene_im;
drawKeypoints(marker_im, keypoints1, display_marker_im, Scalar(0,0,255));
drawKeypoints(scene_im, keypoints2, display_scene_im, Scalar(0,0,255));

SiftDescriptorExtractor extractor;
Mat descriptors1, descriptors2;

extractor.compute( marker_im, keypoints1, descriptors1 );
extractor.compute( scene_im, keypoints2, descriptors2 );

BFMatcher matcher; //BF seems to match better than FLANN
vector< DMatch > matches;
matcher.match( descriptors1, descriptors2, matches );

Mat img_matches;
drawMatches( marker_im, keypoints1, scene_im, keypoints2,
    matches, img_matches, Scalar::all(-1), Scalar::all(-1),
    vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

vector<Point2f> obj, scene;
for (int i = 0; i < matches.size(); i++) {
    obj.push_back(keypoints1[matches[i].queryIdx].pt);
    scene.push_back(keypoints2[matches[i].trainIdx].pt);
}

Mat H;
H = findHomography(obj, scene, CV_RANSAC);

//Get corners of fiducial
vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint(marker_im.cols, 0);
obj_corners[2] = cvPoint(marker_im.cols, marker_im.rows);
obj_corners[3] = cvPoint(0, marker_im.rows);
vector<Point2f> scene_corners(4);

perspectiveTransform(obj_corners, scene_corners, H);

FileStorage fs2("cal.xml", FileStorage::READ);

Mat cameraMatrix, distCoeffs;
fs2["Camera_Matrix"] >> cameraMatrix;
fs2["Distortion_Coefficients"] >> distCoeffs;

Mat rvec, tvec;

//same points as object_corners, just adding z-axis (0)
vector<Point3f> objp(4);
objp[0] = cvPoint3D32f(0,0,0);
objp[1] = cvPoint3D32f(gray.cols, 0, 0);
objp[2] = cvPoint3D32f(gray.cols, gray.rows, 0);
objp[3] = cvPoint3D32f(0, gray.rows, 0);

solvePnPRansac(objp, scene_corners, cameraMatrix, distCoeffs, rvec, tvec );

Mat rotation, viewMatrix(4, 4, CV_64F);
Rodrigues(rvec, rotation);

for(int row=0; row<3; ++row)
{
   for(int col=0; col<3; ++col)
   {
      viewMatrix.at<double>(row, col) = rotation.at<double>(row, col);
   }
   viewMatrix.at<double>(row, 3) = tvec.at<double>(row, 0);
}

viewMatrix.at<double>(3, 3) = 1.0f;

cout << "rotation: " << rotation << endl;
cout << "viewMatrix: " << viewMatrix << endl;

Answer 1

好的，solvePnP()为您提供从模型框架（即立方体）到相机框架（称为视图矩阵）的传递矩阵。

输入参数：

objectPoints - 对象坐标空间中的对象点数组，3xN / Nx3 1通道或1xN / Nx1 3通道，其中N是点数。 std::vector<cv::Point3f>也可以在这里传递。这些点是3D，但由于它们位于（基准标记的）模式坐标系中，因此钻机是平面的，因此每个输入物点的Z坐标为0，
imagePoints - 相应图像点阵列，2xN / Nx2 1通道或1xN / Nx1 2通道，其中N是点数。 std::vector<cv::Point2f>也可以在这里传递，
intrinsics：相机矩阵（焦距，主要点），
distortion：失真系数，假设零失真系数为空，
rvec：输出旋转向量
tvec：输出翻译向量

构建视图矩阵是这样的：

cv::Mat rvec, tvec;
cv::solvePnP(objectPoints, imagePoints, intrinsics, distortion, rvec, tvec);
cv::Mat rotation, viewMatrix(4, 4, CV_64F);
cv::Rodrigues(rvec, rotation);

for(int row=0; row<3; ++row)
{
   for(int col=0; col<3; ++col)
   {
      viewMatrix.at<double>(row, col) = rotation.at<double>(row, col);
   }

   viewMatrix.at<double>(row, 3) = tvec.at<double>(row, 0);
}

viewMatrix.at<double>(3, 3) = 1.0f;

此外，您可以分享您的代码和错误消息吗？

确定相机姿势？

1 个答案: