“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.”