[Greg McKaskle]

I can only comment on what I've seen, but the vision processing approaches I've seen are highly varied but fairly basic.

I've seen teams use NI Vision, OpenCV, RoboRealm, with a few doing custom algorithms.

Most are ad hoc and purpose-made. They only need to know orientation and perhaps distance, and that is all they calculate. It would be great to see robots that detected game elements, field boundaries, friend/foe robots, and navigational obstacles in general, but this is not generally seen as beneficial to the game, so they are largely ignored or are accomplished with other sensors.

Some of the ad hoc approaches are well done, and others are brittle. If you would like to make your knowledge and/or code available to teams, I'd suggest a blog or white paper that analyzes previous games and their vision elements. Perhaps include things like lines on the field, carpet detection, bumper detection. I'd discourage a highly sophisticated black box over an understandable and tinker-able set of widgets that teams can use in many different ways.

And of course you can offer up your opinions and insight on this forum.

Greg McKaskle