Algorithms Allow MAVs to Avoid Obstacles with Single Camera and Neuromorphic Hardware
Yesterday, we posted about some dirt cheap micro air vehicles on Kickstarter. Cheap hardware is great, but to make it do cool stuff, you usually need expensive (or at least, very clever) software. Researchers at Cornell have come up with a way to enable robotic aircraft to navigate around outdoor obstacles using just a single camera and hardware that mimics neuron architecture.
So, why is perceiving obstacles extremely important for aerial robots, and why are current methods based on stereo vision fundamentally limited? Here’s what the researchers have to say:
Perceiving obstacles is extremely important for an aerial robot in order to avoid collisions. Methods based on stereo vision are fundamentally limited by the ﬁnite baseline between the stereo pairs, and fail in textureless regions and in presence of specular reﬂections. Active range-ﬁnding devices are either designed for indoor low-light environments (e.g., the Kinect), or are too heavy for aerial applications. More importantly, they demand more onboard power, which is at a premium for aerial vehicles. [read more..]