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i used the opencv sample code for stereo camera calibration to get the intrinsics and extrinsics of my stereo camera. I used 149 image pairs and the program detected 114 image pairs

Result of my Calibration:

..... 114 pairs have been successfully detected.
Running stereo calibration ...
done with RMS error = 1.60208
average epipolar error = 1.15512

i know the error should be below 1 but i only get below 1 of error in small number of image pairs. so im not sure if my result is good or bad.

You should be able to get an error below 1, but it's not so bad. I also do the calibration with around 100 of images. I often got a few images to discard in which the detection was not reliable.
If you decreased the number of images down to 10 images, then the calibration might overfit for these cases. The error would then not be reliable.

In the calibration process, the problems I faced came from the calibration setup. My recommendations are the following:

• Check that your calibration pattern is perfectly flat. In my case I printed on adhesive paper and glued it on a piece of glass.




• Check that your calibration pattern is not symmetrical in rotation, otherwise the pose estimation could be wrong.




• Check the intermediate pattern points detection. There are some examples in opencv to show the corners or circles centers detected points.




• The error can be also displayed for each frame. This can help you to understand for which images you have a problem. If you see that these images actually have a detection problem, you can discard them.




• If you acquire videos and not images, both cameras should be synchronized with a hardware connection. In my case I cannot have such a link, therefore I built some kind of holder for the calibration target to keep it still, and I acquired only images, not videos.




• This won't reduce your calibration error, but use very different pattern positions to cover the maximum of the field of view.




• If your depth of field is small and you have blurry images before/after the focus because of that, change from the chessboard pattern to a circles pattern (functions also available in opencv).




• If you don't have a strong distortion in your images (e.g. a photo with an iphone doesn't really show a strong fisheye-like distortion), consider forcing K3=0.




• In my case, I fixed the "principal point" in the middle of the image, because the algorithm always found crazy values for these parameters, like for K3.

Hope this helps a bit. Good luck!