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Most Common Mistakes when doing Stereo Calibration

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Most Common Mistakes when doing Stereo Calibration

While making my stereo rig, it took me 15 tries to make it right. It started to get really depressing, but every time I learned something new. Below are a few most common mistakes I made while taking calibration images for stereo vision.

Not enough images - when calculating projection matrices and distortions, the more images there are the better. Not enough images result in a bad projection matrix therefore worse results. There should be at least 10 images for calibration, but for just to be on the safe side make it 20 or 30. More than that might again corrupt your results.

Bad pattern coverage on the image - sample images should contain chessboard on about every position in the image. In all of the corners, sides, center and also under different angles (pitch, yaw and roll). Not having that kind of variety of images means bigger errors on parts of the images not covered.

Bad lighting - as everything in computer vision, good lighting is essential! Dark areas usually won't get calculated at all. That's one of the reasons Kinect uses IR light.

Low resolution - while taking calibration images, it's extremely important that the pattern is correctly and accurately detected. When going further away, squares get smaller consequently corners of the squares aren't touching any more as shown on the image bellow. The algorithm when searching for the points of interest can now choose between two different points, skyrocketing error which nobody wants.

Print that chessboard! - My first tries were taking images where pattern was displayed on a tablet. Trying to be environmental and all. The problem that occurred was the same as with the low resolution, only much worse. White areas on the screen were radiating that much light that black squares were starting to erode, causing the effect as shown on the image bellow.

calib_err

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