Kinect2.0相机标定

尝试进行Kinect2.0相机进行标定

1. Color镜头标定

$(u_{rgb},v_{rgb},1)=W_{rgb}*(x,y,z)$

Calibration results after optimization (with uncertainties):

Focal Length:          fc = [ 1094.03583   1087.37528 ] +/- [ 55.02816   51.42175 ]

Principal point:       cc = [ 942.00992   530.35240 ] +/- [ 13.00131   31.27892 ]

Skew:             alpha_c = [ 0.00000 ] +/- [ 0.00000  ]   => angle of pixel axes = 90.00000 +/- 0.00000 degrees

Distortion:            kc = [ 0.06857   -0.10542   0.00233   0.00092  0.00000 ] +/- [ 0.02206   0.02884   0.00379   0.00492  0.00000 ]

Pixel error:          err = [ 0.49343   0.67737 ]

2. 红外镜头标定

$(u_{ir},v_{ir},1)=W_{ir}*(x,y,z)$

Calibration results after optimization (with uncertainties):

Focal Length:          fc = [ 379.40726   378.54472 ] +/- [ 40.73354   34.75290 ]

Principal point:       cc = [ 263.73696   201.72450 ] +/- [ 9.17740   30.29723 ]

Skew:             alpha_c = [ 0.00000 ] +/- [ 0.00000  ]   => angle of pixel axes = 90.00000 +/- 0.00000 degrees

Distortion:            kc = [ 0.03377   -0.04195   0.00519   0.00734  0.00000 ] +/- [ 0.07368   0.25678   0.01111   0.00965  0.00000 ]

Pixel error:          err = [ 0.88997   0.92779 ]

根据上面两个式子可以推导出两个图像像素之间的对应关系。先将RGB图像映射和depth同样大小。

3. 2个相机相对关系计算

前面两个相机都是以相机中心作为世界坐标系的原点。要建立两个相机之间的关系，需要构建以统一的世界坐标系。以深度相机中心为世界坐标的原点。

则RGB相机和相机原点存在如下关系

$(x_{w},y_{w},z_{w})=(x_{ir},y_{ir},z_{ir})=R*(x_{rgb},y_{rgb},z_{rgb})+T$

补充：两个相机的配准问题当前的好多博客里写的方法，根据color和Ir 的外参计算的说法很扯淡。

因为2个图像的分辨率不同，外参根本不在一个框架下。这应该是一个立体匹配的问题。

A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies
https://github.com/rgbdemo/rgbdemo/blob/master/calibration/calibrate_kinect.cpp