Click For Photo: https://www.sciencedaily.com/images/2018/02/180212084510_1_540x360.jpg
Many existing approaches rely on intricate maps that aim to tell drones exactly where they are relative to obstacles, which isn't particularly practical in real-world settings with unpredictable objects. If their estimated location is off by even just a small margin, they can easily crash.
With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed NanoMap, a system that allows drones to consistently fly 20 miles per hour through dense environments like forests and warehouses.
NanoMap - Insights - System - Drone - Position
One of NanoMap's key insights is a surprisingly simple one: the system considers the drone's position in the world over time to be uncertain, and actually models and accounts for that uncertainty.
"Overly confident maps won't help you if you want drones that can operate at higher speeds in human environments," says graduate student Pete Florence, lead author on a new related paper. "An approach that is better aware of uncertainty gets us a much higher level of reliability in terms of being able to fly in close quarters and avoid obstacles."
NanoMap - System - Series - Measurements - Drone
Specifically, NanoMap uses a depth-sensing system to stitch together a series of measurements about the drone's immediate surroundings. This allows it to not only make motion plans for its current field of view, but also anticipate how it should move around in the hidden fields of view that it has already seen.
"It's kind of like saving all of the images you've seen of the world as a big tape in your head," says Florence. "For the drone to plan motions, it essentially goes back into time to think individually of all the different places that it was in."
Team - Tests - Impact - Uncertainty - Example
The team's tests demonstrate the impact of uncertainty. For example, if NanoMap wasn't modeling uncertainty and the drone drifted just five percent away from where it was expected to be, the drone would...
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