A little uncertainty can help drones dodge obstacles at high speeds, says MIT

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.

But one way to bypass these requirements, says CSAIL’s Peter Florence, is to plan less and react more. Florence and his team have developed an alternate method of navigation and obstacle avoidance that is tuned to these demands, which they call NanoMap. It still works by collecting 3D data about the environment, but this information is never fused into a single map and is instead stored in a series of snapshots. This allows for faster reaction times: the drone is processing less information each second (that’s the uncertainty aspect), and so crunches the data with ease.

“Because we’re not taking hundreds of measurements and fusing them together, it’s really fast,” Florence tells The Verge. “And when we want to plan our way around the world, we just search back through the views we already have.”

There are drawbacks to this method, too, and Florence says NanoMap isn’t great for applications that need high-quality maps of their surroundings (think, drones doing surveying work in agriculture or helping with search-and-rescue missions). Similarly, the makers of self-driving cars will likely be happier with SLAM, given that its hardware demands are less of a burden in vehicles that are going to be big and expensive anyway.

But, says Florence, for small drones, NanoMap could be the perfect way to give them obstacle-sensing abilities without overtaxing their digital brains. Early tests in both real-life environments and simulations are promising.

“We have drones that can fly around now, but there’s nothing that’s as good as a hawk, for example, that can just blaze through a forest at top speeds,” says Florence. “When you’re going that fast, you just can’t expect to have perfect geometric measurements of the world. You need something else.”

“I think just working on robots that can move really fast and gracefully is fun, so that’s the main thing.”