Question

我用于内置相机校准CALDE工具（http://www.dlr.de/rm/en/desktopdefault.aspx/tabid-3925/），它非常强大，我建议大家使用它来进行精确的内部相机校准。

Calde最后给了我一个看起来像这样的文件：

% CAMERA # 1
% 

% Image size:
imagesize_1 = [ 640 480 ]

% Focal length:
fc_1 = [ 537.417 537.311 ]

% Principal point:
cc_1 = [ 314.329 239.206 ]

% Skew (please note Skew = Gamma/ScaleX):
alpha_c_1 = [ 0.00168813 ]

% Distortion (radial, decentering, and thin-prism, if any)
% (only kc_ is present in Bouguets toolbox, in that order)
kc_1 = [ 0.0450068 -0.144093 0.00000 0.00000 0.00000 ]
radial_1 = [ 0.0450068 -0.144093 0.00000 ]
decentering_1 = [ 0.00000 0.00000 0.00000 ]
thinprism_1 = [ 0.00000 0.00000 0.00000 ]

% TCP_T_CAMERA:
TCP_T_CAMERA1 = [ ...
 1.00000 0.00000 0.00000 0.00000 ;
 0.00000 1.00000 0.00000 0.00000 ;
 0.00000 0.00000 1.00000 0.00000 ]


% MAINCAMERA_T_OBJECT:

MAINCAMERA_T_OBJECT(1:3,1:4,1) = [ ...
 0.973021 0.118707 -0.197836 -41.0266 ;
 -0.0730198 0.971849 0.224004 -11.4107 ;
 0.218857 -0.203514 0.954297 662.759 ]

MAINCAMERA_T_OBJECT(1:3,1:4,2) = [ ...
 0.999266 0.0369076 0.0102493 76.4807 ;
 -0.0381896 0.980636 0.192078 116.143 ;
 -0.00296168 -0.192328 0.981326 1084.49 ]

MAINCAMERA_T_OBJECT(1:3,1:4,3) = [ ...
 0.992568 -0.0105678 -0.121230 106.536 ;
 0.0316905 0.984295 0.173663 -159.832 ;
 0.117491 -0.176215 0.977315 1087.52 ]

MAINCAMERA_T_OBJECT(1:3,1:4,4) = [ ...
 0.877250 -0.0191499 0.479652 166.351 ;
 -0.0501447 0.990082 0.131240 -29.7432 ;
 -0.477408 -0.139182 0.867589 988.947 ]

MAINCAMERA_T_OBJECT(1:3,1:4,5) = [ ...
 0.527366 -0.00319134 0.849632 144.484 ;
 -0.124694 0.988874 0.0811117 -24.5847 ;
 -0.840438 -0.148719 0.521101 969.772 ]

MAINCAMERA_T_OBJECT(1:3,1:4,6) = [ ...
 0.891724 0.0522552 -0.449552 94.2428 ;
 0.0213817 0.987339 0.157179 -0.458213 ;
 0.452074 -0.149773 0.879317 1082.62 ]

MAINCAMERA_T_OBJECT(1:3,1:4,7) = [ ...
 0.693097 0.0722656 -0.717212 76.8902 ;
 0.0699173 0.983531 0.166666 -17.6251 ;
 0.717445 -0.165661 0.676631 1050.90 ]

MAINCAMERA_T_OBJECT(1:3,1:4,8) = [ ...
 0.985416 0.0898682 -0.144496 -31.7523 ;
 -0.0664750 0.984994 0.159272 140.445 ;
 0.156641 -0.147344 0.976603 1565.78 ]

MAINCAMERA_T_OBJECT(1:3,1:4,9) = [ ...
 0.992972 0.0389569 -0.111755 -211.173 ;
 -0.0133414 0.975099 0.221369 228.341 ;
 0.117596 -0.218323 0.968765 1974.26 ]

MAINCAMERA_T_OBJECT(1:3,1:4,10) = [ ...
 0.997039 0.0730305 -0.0240702 -26.9394 ;
 -0.0675975 0.981645 0.178339 -45.1356 ;
 0.0366525 -0.176183 0.983675 706.637 ]

MAINCAMERA_T_OBJECT(1:3,1:4,11) = [ ...
 0.998929 0.0272116 -0.0374343 -5.70587 ;
 -0.0215852 0.989451 0.143250 -2.35603 ;
 0.0409375 -0.142289 0.988978 647.193 ]

MAINCAMERA_T_OBJECT(1:3,1:4,12) = [ ...
 0.987303 0.121919 -0.101829 -177.281 ;
 0.00962968 0.593922 0.804465 91.9028 ;
 0.158558 -0.795231 0.585206 1072.55 ]

现在我需要将这些数据存储在ros camera_info消息中，如下所示：见这里：http://docs.ros.org/kinetic/api/sensor_msgs/html/msg/CameraInfo.html

The distortion model used. Supported models are listed in
sensor_msgs/distortion_models.h. For most cameras, "plumb_bob" - a
simple model of radial and tangential distortion - is sufficient.
string distortion_model

The distortion parameters, size depending on the distortion model.
For "plumb_bob", the 5 parameters are: (k1, k2, t1, t2, k3).
float64[] D

Intrinsic camera matrix for the raw (distorted) images.
     [fx  0 cx]
 K = [ 0 fy cy]
     [ 0  0  1]
Projects 3D points in the camera coordinate frame to 2D pixel
coordinates using the focal lengths (fx, fy) and principal point
(cx, cy).
float64[9]  K # 3x3 row-major matrix

Rectification matrix (stereo cameras only)
A rotation matrix aligning the camera coordinate system to the ideal
stereo image plane so that epipolar lines in both stereo images are
parallel.
float64[9]  R # 3x3 row-major matrix

Projection/camera matrix
    [fx'  0  cx' Tx]
 P = [ 0  fy' cy' Ty]
    [ 0   0   1   0]
 By convention, this matrix specifies the intrinsic (camera) matrix
  of the processed (rectified) image. That is, the left 3x3 portion
  is the normal camera intrinsic matrix for the rectified image.
 It projects 3D points in the camera coordinate frame to 2D pixel
  coordinates using the focal lengths (fx', fy') and principal point
 (cx', cy') - these may differ from the values in K.
 For monocular cameras, Tx = Ty = 0. Normally, monocular cameras will
  also have R = the identity and P[1:3,1:3] = K.
 For a stereo pair, the fourth column [Tx Ty 0]' is related to the
  position of the optical center of the second camera in the first
  camera's frame. We assume Tz = 0 so both cameras are in the same
  stereo image plane. The first camera always has Tx = Ty = 0. For
  the right (second) camera of a horizontal stereo pair, Ty = 0 and
  Tx = -fx' * B, where B is the baseline between the cameras.
 Given a 3D point [X Y Z]', the projection (x, y) of the point onto
 the rectified image is given by:
  [u v w]' = P * [X Y Z 1]'
         x = u / w
         y = v / w
  This holds for both images of a stereo pair.
float64[12] P # 3x4 row-major matrix

我不知道如何从calde数据中获取P矩阵？

Answer 1

似乎您有一台相机。在P矩阵中：

fx：fc_1中的第一个值。

fy：fc_1中的第二个值。

cx和cy：cc_1中的值与f的顺序相同。

如果您只有一台摄像机，则文档会说Tx和Ty为0。

关于变形参数，在ROS文档中，带有k的变形参数是径向变形参数，t是切向变形参数（铅锤是径向和切向变形的组合）。由于CALDE工具仅计算前两个径向变形参数，因此可以按相同顺序使用它们。

精确的内部相机校准 - 用于ROS camera_info消息

1 个答案: