Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo


Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

“What we find in mammals are these cells called ‘place’ cells,” says Melhuish. In rats, these cells, which reside in the hippocampus, have been shown to fire in distinct patterns depending on the animal’s location, he says. Indeed, there’s a lot of interest in trying to copy biological models in robotics, says Melhuish, since they often appear to work so well.

Traditional SLAM solutions tend to use a robot’s sensors to continuously construct geometric maps of its surroundings or to create symbolic representations of features around the robot. But with these approaches comes a trade-off, says Tapus: if it’s more precise, the robot may have more difficulty recognizing it at a later stage, but if it’s not precise enough, it might be too easily confused with other places.

The cognitive fingerprints avoid this by providing a robust and effective way of representing locations in a way that requires few computational resources. In addition, because they still maintain the relative positions of landmarks, it’s easy to use probabilistic algorithms to reliably match places, even if the robot is not positioned in precisely the same place or if some of the objects in the environment have moved.

This could prove particularly useful for car navigation systems, for although GPS is sufficient for coarse positioning, says Tapus, often it’s useful to know the position of the robot or vehicle with respect to buildings, trees, and intersections. For this, a more refined technique is required, particularly when it comes to things that move, such as people.

Even if Tapus’s approach proves useful, though, it may be hard to say how closely it resembles human problem solving. Davison, for one, cautions against making too strong a comparison. “As computing power increases,” he says, “it is often hard to tell whether the algorithms being used successfully in robotics and computer vision have much relation with how the human brain solves these problems.”

0 comments about this story. Start the discussion »

Tagged: Computing

Reprints and Permissions | Send feedback to the editor

From the Archives


Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me