Neuromorphic Algorithms Allow MAVs To Avoid Obstacles With Single Camera 39
First time accepted submitter aurtherdent2000 writes "IEEE Spectrum magazine says that Cornell University has developed neuromorphic algorithms that enable MAVs to avoid obstacles using just a single camera. This is especially relevant for small and cheap robots, because all you need is a single camera, minimal processing power, and even more minimal battery power. Now, will we see more of the drones and aerial vehicles flying all around us?"
processing power... (Score:4, Insightful)
I don't think that phrase invokes the same idea as most of the folks on /. The "neuromorphic" algorithms they allude to are the kind that run on highly specialized hardware (e.g., this beast [modha.org]). This type of hardware really just works similarly to synapses (integrate & fire architecture). Of course you could simulate the algorithm on a more conventional processor, but it would probably lose much of it's low-power attribute.
FWIW, the algorithm they propose is attempt to identify objects that project up from the ground. To do this, they attempt to label parts of the image as obstacle (or not) taking a raw initial guess and filtering it with a pre-trained neural net (using some sort of adjacent region belief propagation technique).
I think they may have "cheated" a bit in that in some papers, they describe decomposing the image with oriented Gabor filters (edge orientation detectors), but they admit that this decompsition doesn't currently work well on their ultra-low-power computing platform.
FYI: MAV=micro aerial vehicle