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ros - 精确的内在相机校准 - 用于 ROS camera_info 消息

问题描述

我使用 CALDE 工具（http://www.dlr.de/rm/en/desktopdefault.aspx/tabid-3925/）进行内在相机校准，它非常强大，我建议大家使用它来进行精确的内在相机校准.

Calde 最后给了我一个如下所示的文件：

% 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如下所示的 ros camera_info 消息中：请参见此处：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 的数据中获取 P 矩阵？

标签： roscamera-calibration

解决方案

看来你只有一个相机。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。

如果你有一个相机，文档说 Tx 和 Ty 为 0。

关于畸变参数，在 ROS 文档中，k 是径向畸变参数，t 是切向畸变参数（铅锤是径向和切向畸变的组合）。由于您的 CALDE 工具仅计算前两个径向畸变参数，您可以按相同顺序使用它们。

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