Things have been super crazy with work and finishing my thesis so I've forgotten to update things. I wanted to get back to my discussion on structured light since I did the intro to that and didn't finish it. I also want to commit to posting more often so I can showcase the cool work that I've been doing.
So on to camera calibration.....
There are quite a few great tools out there that do camera calibration for you, one example is the GML camera calibration toolbox, which can automatically detect grid corners and solve for an excellent calibration. But I ran into an interesting issue with this toolkit that made it insufficient for structured light calibration.
When performing laser calibration and motor calibration, it is necessary to calculate an extrinsic transformation between the camera and the calibration pattern. What I found with GML was that this extrinsic transformation was not stable. I discovered this when I couldn't calibrate motor motion with extrinsic values extracted using GML. This manifested itself in large variation in extrinsic parameter displacements for small constant motor displacements. I then manually selected the checkerboard corners with the MatLab camera calibration toolbox and found much more consistent extrinsic parameters. My guess is this is due to the optimization routines used in GML.
So with that being said, the right combination of MatLab toolboxes and some custom code can make a completely automated calibration routine. Today we'll talk about camera calibration.
For just camera calibration, we can use AMCC toolbox. This is a modification to the MatLab camera calibration toolbox to include automatic checkerboard extraction, which I consider a must since manually selecting checkerboard corners is a huge pain.
First print out a checkerboard pattern and attach it to a rigid flat surface. I like to use the patterns provided with GML in its install directory since they're pre-made.
Collect images of the pattern with your camera with a consistent naming convention. I like to call them Left-*.jpg and Right-*.jpg when using a stereo setup, and just Image-*.jpg otherwise.
The images should look similar to this (without the laser for now though)
Keep in mind the pixel error values really really should be between 0.1 and 1. If they aren't then there is something very wrong.
Things that can go wrong include:
1) Blurry / poorly exposed images.
2) Incorrect input number of corners, or incorrect correspondences. (Checkerboard shouldn't be too skewed from the image sensor)
The amcc toolbox is fairly robust, thus it will likely reject bad images and still provide a good result, also since it's automatic, there's no frustration after hand selecting 130 images to find half of them are too blurry or dark. (Special project involving NIR cameras)
Next I'll discuss how to calibrate cameras with OpenCV for those who don't have Matlab.
Collect images of the pattern with your camera with a consistent naming convention. I like to call them Left-*.jpg and Right-*.jpg when using a stereo setup, and just Image-*.jpg otherwise.
The images should look similar to this (without the laser for now though)
Place about twenty images in different poses into a folder. Enter that folder with Matlab and edit auto_mono_calibrator_efficient (provided by AMCC toolbox).
Add / edit these lines at the top of auto_mono_calibrator_efficient.
dX = 18.7452;
dY = 18.7452;
nx_crnrs = 7;
ny_crnrs = 4;
proj_tol = 2.0;
format_image = 'jpg';
calib_name = 'Image-';
Where dX and dY are the measured sizes of the checkerboard squares in mm. nx_crnrs and ny_crnrs are the number of checkerboard corners along each axis. format_image is obviously the image format, and calib_name is the base name of the image.
Now run auto_mono_calibrator_efficient and it should spit out the calibration data for this camera.
For a Logitech webcam running at 640x480, I got the following results:
Focal Length: fc = [ 783.70794 771.22158 ] ± [ 8.81920 9.13420 ]
Principal point: cc = [ 237.23567 270.25039 ] ± [ 12.64706 6.05165 ]
Skew: alpha_c = [ 0.00000 ] ± [ 0.00000 ] => angle of pixel axes = 90.00000 ± 0.00000 degrees
Distortion: kc = [ -0.06032 0.06316 -0.00330 -0.02552 0.00000 ] ± [ 0.02146 0.06905 0.00272 0.00393 0.00000 ]
Pixel error: err = [ 0.20168 0.14721 ]
Keep in mind the pixel error values really really should be between 0.1 and 1. If they aren't then there is something very wrong.
Things that can go wrong include:
1) Blurry / poorly exposed images.
2) Incorrect input number of corners, or incorrect correspondences. (Checkerboard shouldn't be too skewed from the image sensor)
The amcc toolbox is fairly robust, thus it will likely reject bad images and still provide a good result, also since it's automatic, there's no frustration after hand selecting 130 images to find half of them are too blurry or dark. (Special project involving NIR cameras)
Next I'll discuss how to calibrate cameras with OpenCV for those who don't have Matlab.
