[David Lame]

Like most engineering decisions, there isn't a "good" and "bad", but there is often a tradeoff.

We used GRIP on a laptop with an axis camera? Why? Because our code for vision was 100% student built, and the student had never done computer vision before. GRIP on the laptop was the easiest to get going, and it only worked with an i.p. camera.

There are down sides to that. If you use opencv, you can write much more flexible code that can do more sophisticated processing, but it's harder to get going. On the other hand, by doing things the way we did, we had some latency and frame rate issues. We couldn't go beyond basic capture of the target, and we had to be cautious about the way we drove when under camera control.

Coprocessors, such as a Raspberry PI, TK1, or TX1, (I was sufficiently impressed with the NVIDIA products that I bought some of the company's stock), will allow you a lot more flexibility, but you have to learn to crawl before you can walk. Those products are harder to set up and have integration issues. It's nothing dramatic, but when you have to learn the computer vision algorithms, and the networking, and how to power up a coprocessor, and do it all at the same time, it gets difficult.

If you are trying to prepare for next year, or dare I say it for a career that involves computer vision, I would recommend grip on the laptop as a starting point, because you can experiment with it and see what happens without even hooking to the robot. After you have that down, port it to a PI or an NVIDIA product. The PI probably has the most documentation and example work, so that's probably a good choice, not to mention that the whole setup, including camera, is less than 100 bucks.

Once you get that going, the sky's the limit.