Ground truth, that is.
I'm working on a vision-based gyroscope, in hopes of getting a really solid heading-hold on my quad, without the use of magnetometers. For prototyping purposes, I'm using Python and OpenCV to test the effectiveness of the various approaches I'm considering. To do that, I'll need ground truth data. I attached a coaster to the shaft of a hobby rc servo as a target, and mounted this assembly onto a piece of 2x6 connected one of these. The idea is that I can rotate the little disc in a known, repeatable way, then compare the known rotations to the output of my vision-based gyro program. Here's a diagram (because I love diagrams):
I'm working on a vision-based gyroscope, in hopes of getting a really solid heading-hold on my quad, without the use of magnetometers. For prototyping purposes, I'm using Python and OpenCV to test the effectiveness of the various approaches I'm considering. To do that, I'll need ground truth data. I attached a coaster to the shaft of a hobby rc servo as a target, and mounted this assembly onto a piece of 2x6 connected one of these. The idea is that I can rotate the little disc in a known, repeatable way, then compare the known rotations to the output of my vision-based gyro program. Here's a diagram (because I love diagrams):
Here's some video of the thing. The program you see running on the screen is OpenCV's Lucas-Kanade tracker demo. The little green streamers are the feature tracks that the program is following :
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