Washington: One of the reasons why self-driving cars and mini-helicopters are not delivering online purchases is that autonomous vehicles tend not to perform well under pressure.
A system that can flawlessly parallel park at 5 mph may have trouble avoiding obstacles at 35 mph.
Andrea Censi, a research scientist in MIT`s Laboratory for Information and Decision Systems, thinks the solution could be to supplement cameras with a new type of sensor called an event-based (or "neuromorphic") sensor, which can take measurements a million times a second.
Censi said in a regular camera, one has an array of sensors, and then there is a clock.
He said that if one has a 30-frames-per-second camera, every 33 milliseconds the clock freezes all the values, and then the values are read in order." With an event-based sensor, by contrast, "each pixel acts as an independent sensor.
Censi said when a change in luminance - in either the plus or minus direction - is larger than a threshold, the pixel says, `I see something interesting` and communicates this information as an event. And then it waits until it sees another change.
Davide Scaramuzza, of the University of Zurich present the first state-estimation algorithm, and Censi`s algorithm supplements camera data with events reported by an event-based sensor, which was designed by their collaborator Tobi Delbruck of the Institute for Neuroinformatics in Zurich.
The new algorithm`s first advantage is that it doesn`t have to identify features: Every event is intrinsically a change in luminance, which is what defines a feature. And because the events are reported so rapidly - every millionth of a second - the matching problem becomes much simpler.
There aren`t as many candidate features to consider because the robot can`t have moved very far.