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I am trying to calibrate a camera using a checkerboard by the well known Zhang's method followed by bundle adjustment, which is available in both Matlab and OpenCV. There are a lot of empirical guidelines but from my personal experience the accuracy is pretty random. It could sometimes be really good but also sometimes really bad. The result actually can vary quite a bit just by simply placing the checkerboard at different locations. Suppose the target camera is rectilinear with 110 degree horizontal FOV.

  1. Does the number of squares in the checkerboard affect the accuracy? Zhang uses 8x8 in his original paper without really explaining why.

  2. Does the length of the square affect the accuracy? Zhang uses 17cm x 17cm without really explaining why.

  3. What is the optimal number of snap shots of different checkerboard position/orientation? Zhang uses 5 images only. I saw people suggesting 20~30 images with checkerboards at various angles, fills the entire field of view, tilted to the left, right, top and bottom, and suggested there should be no checkerboard placed at similar position/orientation otherwise the result will be biased towards that position/orientation. Is this correct?

The goal is to figure out a workflow to get consistent calibration result.

Dima
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user3667089
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2 Answers2

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If the accuracy you get is "pretty random" then you are likely not doing it right: with stable optics and a well conducted procedure you should consistently be getting RMS projection errors within a few tenths of a pixel. Whether this corresponds to variances of millimeters or meters in 3D space depends, of course, on your optics and sensor resolution (calibration is not a way around physics).

I wrote time ago a few suggestions in this answer, and I recommend you follow them. In particular, pay attention to locking the focus distance (I have seen & heard countless people trying to calibrate a camera on autofocus, and be sorely disappointed). As for the size of the target, again it depends on your optics and camera resolution, but generally speaking the goals are (1) to fill with measurements both the field of view and the volume of space you'll be working with, and (2) to observe significant perspective foreshortening, because that is what constrains the solution for the FOV. Good luck!

[Ed.to address a comment]

Concerning variations on the parameter values across successive calibrations, the first thing I'd do is calculate the cross RMS errors, i.e. the RMS error on dataset 1 with the camera calibrated on dataset 2, and vice versa. If either is significantly higher than the calibration errors, it's an indication that the camera has changed between the two calibrations and so all odds are off. Do you have auto-{focus,iris,zoom,stabilization} on? Turn them all off: auto-anything is the bane of calibration, with the only exception of exposure time. Otherwise, you need to see if the variations you observe on the parameters are actually meaningful (hint, they often are not). A variation of the focal length in pixels of several parts per thousand is probably irrelevant with today's sensor resolutions - you can verify that by expressing it in mm, and comparing it to the dot pitch of the sensor. Also, variations of the position of the principal point in the order of tens of pixels are common, since it is poorly observed unless your calibration procedure is very carefully designed to estimate it.

Francesco Callari
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  • I can take 20 checkerboard images and I get a combination of parameters, which are focal points, principle points, and distortion parameters, that has low RMS projection errors with respect to the given checkerboard images. The problem is if I take another 20 checkerboard images I get parameters that still has low RMS projection errors with respect to the given checkerboard images but could vary quite a bit from the parameters determined previously. The question is how do I know which set of parameters is the right one? What's the correct workflow that can guarantee a consistent result? – user3667089 Jul 01 '15 at 01:01
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Ideally you want to place your checkerboard at roughly the same distance from the the camera, as the distance at which you want to do your measurements. So your checkerboard squares must be large enough to be resolvable from that distance. You also do want to cover the entire field of view with points, especially close to the edges and corners of the frame. Also, the smaller the board is, the more images you should take to cover the entire field of view. So 20-30 images is usually a good rule of thumb.

Another thing is that the checkerboard should be asymmetric. Ideally, you want to have an even number of squares along one side, and an odd number of squares along the other. This way the board's in-plane orientation is unambiguous.

Also, I would suggest that you try the Camera Calibrator app in MATLAB. At the very least, look at the documentation, which has a lot of useful suggestions for calibrating cameras.

Dima
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    The distance I will do my measurement is not confined to a small region. It's probably about from 2m ~ 50m. Could you elaborate on the math side on why "Ideally you want to place your checkerboard at roughly the same distance from the the camera, as the distance at which you want to do your measurements."? I thought the distance shouldn't matter. Also thanks for the great suggestion. – user3667089 Jul 01 '15 at 00:44
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    In theory, the distance doesn't matter. In reality, you have noise, quantization errors, roundoff errors, etc., so you want to calibrate as close to where you are going to do your measurements as you can. – Dima Jul 01 '15 at 21:29