Neuromorphic Algorithms Allow MAVs To Avoid Obstacles With Single Camera
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?"
I'm not sure what a MAV is....
Googling...
http://en.wikipedia.org/wiki/Micro_air_vehicle
Would it have killed the editors to define that?
I went to school with a girl who had no depth perception what-so-ever. She had three accidents in 2 years before anyone realized that she couldn't tell how far away things were. I don't think I want a autonomous drone flying above my head like that.
I would be interested to know if this robot suffers the same problem as birds do when they fly into windows. I might just pay good money to see a pack of drones crash into a glass building.
Looks really clumsy. The thing has no idea of the space around it, barely managing to dodge at the last moment.
How does the robot know a certain location is not traversable? I know it is possible to use one camera and a large database of things to get even a 3d guess of its environment without moving. One camera and moving, and suddenly you have all the data to work with. The problem is, no one has developed software that you walk around a building with a video camera, and it becomes a quake level. So unless they did that, I'd be interested in how they find out what is not traversable.
God spoke to me
Cause they will need to engineer anti-drone drones now that everyone can afford drones.
Sig. Sig. Sputnik
I guess depth perception is overrated.
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). 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
This is the logic behind a flinch reflex. It's just enough approaching obstacle detection to avoid hitting stuff. It's good to have in a UAV that has to operate near obstacles. It's not full SLAM, but it doesn't need to be.
Nice. Now get it into the toy helicopter market.
I trust the group presenting this, but I could not verify their conclusions.