Quote:
Originally Posted by sparkytwd
I wouldn't take a single implementation as setting the bar for what's possible. Even setting aside the CUDA cores, 4 2ghz ARMv7 cores are quite capable.
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To my understanding, 900 was the first team to implement a complete machine learning based vision solution. OpenCV is not regarded as a machine (deep) learning library. It seems only natural to switch to a library that has at least an emphasis on this, instead of an after thought.
Their implementation, cascade training, is an extremely light version of machine learning by comparison, and they were getting 15 fps. Unless teams are going to start putting *entire computers on their robot, and struggle to
reliably power it off of the PDB as well as dedicate that much space, something has to change. Also cost must be considered for a computer; Between a motherboard, memory, cpu and gpu, it adds up fast.
You could always off-board everything, but then you're limiting yourself to the bandwidth limit.
*In 2012, 1706 did have an entire computer on their robot. It had 8 gb of ram, an i5 and ran ubuntu. We were averaging 20 fps (though we were doing a real time pose calculation, so that's actually really good with everything considered). I personally don't recommend unless absolutely needed.