From MIT News:
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 such as forests and warehouses.
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.”
Read more and see more from MITCSAIL on YouTube
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