我正在尝试从计算的基本矩阵中获取平移和旋转向量。我使用OpenCV,通用方法来源于维基百科。我的代码如下:
我正在尝试从计算的基本矩阵中获取平移和旋转向量。我使用OpenCV,通用方法来源于维基百科。我的代码如下:
//Compute Essential Matrix
Mat A = cameraMatrix(); //Computed using chessboard
Mat F = fundamentalMatrix(); //Computed using matching keypoints
Mat E = A.t() * F * A;
//Perfrom SVD on E
SVD decomp = SVD(E);
//U
Mat U = decomp.u;
//S
Mat S(3, 3, CV_64F, Scalar(0));
S.at<double>(0, 0) = decomp.w.at<double>(0, 0);
S.at<double>(1, 1) = decomp.w.at<double>(0, 1);
S.at<double>(2, 2) = decomp.w.at<double>(0, 2);
//V
Mat V = decomp.vt; //Needs to be decomp.vt.t(); (transpose once more)
//W
Mat W(3, 3, CV_64F, Scalar(0));
W.at<double>(0, 1) = -1;
W.at<double>(1, 0) = 1;
W.at<double>(2, 2) = 1;
cout << "computed rotation: " << endl;
cout << U * W.t() * V.t() << endl;
cout << "real rotation:" << endl;
Mat rot;
Rodrigues(images[1].rvec - images[0].rvec, rot); //Difference between known rotations
cout << rot << endl;
最后,我试图将估计的旋转与使用每个图像中的棋盘计算的旋转进行比较(我计划在没有棋盘的情况下获取外参参数)。例如,我得到了这个:
computed rotation:
[0.8543027125286542, -0.382437675069228, 0.352006107978011;
0.3969758209413922, 0.9172325022900715, 0.03308676972148356;
0.3355250705298953, -0.1114717965690797, -0.9354127247453767]
real rotation:
[0.9998572365450219, 0.01122579241510944, 0.01262886032882241;
-0.0114034800333517, 0.9998357441946927, 0.01408706050863871;
-0.01246864754818991, -0.01422906234781374, 0.9998210172891051]
很明显存在问题,我只是找不出它可能是什么。
编辑: 这是我用未转置的vt(显然来自另一个场景)得到的结果:
computed rotation:
[0.8720599858028177, -0.1867080200550876, 0.4523842353671251;
0.141182538980452, 0.9810442195058469, 0.1327393312518831;
-0.4685924368239661, -0.05188790438313154, 0.8818893204535954]
real rotation
[0.8670861432556456, -0.427294988334106, 0.2560871201732064;
0.4024551137989086, 0.9038194629873437, 0.1453969040329854;
-0.2935838918455123, -0.02300806966752995, 0.9556563855167906]
这是我的计算出的相机矩阵,误差相当小(大约0.17...)。
[1699.001342509651, 0, 834.2587265398068;
0, 1696.645251354618, 607.1292618175946;
0, 0, 1]
这是我在尝试重新投影一个立方体时得到的结果... 相机0,立方体是轴对齐的,旋转和平移为(0, 0, 0)。 图像 http://imageshack.us/a/img802/5292/bildschirmfoto20130110u.png
还有另一张图片,显示了第一张图片中点的极线。 图像 http://imageshack.us/a/img546/189/bildschirmfoto20130110uy.png
decomp.vt
是 V 的转置,而不是 V。如果你说U * W.t() * V
,你会得到什么? - yiding