A little Uncertainty can help Drones dodge Obstacles at High Speeds, says MIT By James Vincent @jjvincent in TheVerge
For drones trying to navigate a busy environment like a warehouse or a forest at high speed, the ability to know exactly where they are at all times would seem pretty essential. Not so, say researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who have a devised a new, efficient way to guide drones around obstacles. The key ingredient? Uncertainty.
With most drones — and, indeed, most self-driving vehicles — navigation starts with a map. To draw one, depth sensors are used to scan the immediate environment which is compiled into a single 3D model. This then tells the vehicle not only where they are at any given moment, but also how to get to their destination. It’s a method commonly known as “simultaneous localization and mapping,” or SLAM.
SLAM has served the community pretty well to date, but it has its downsides. For one, it’s a very intensive process, that needs lots of high-fidelity data and computing power to process it. This is why Waymo and Uber’s recently settled lawsuit was all about LIDAR — the laser-firing sensors used to collect and process depth data. Data is important.
But, this process creates problems at high speeds and with small crafts like drones. They don’t have the time to collect all the data they need, and giving them the processors to understand it all is expensive. .... "
