MIT’s New Vision System Could Revolutionize How Household Robots Function

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Robot arm by kalhh via Pixabay, licensed under CC0 1.0

MIT researchers have discovered an “off-the-shelf” algorithm to aggregate different perspectives, capable of providing robots the ability to recognize four times as many objects compared to others that only utilize one perspective.

But they didn’t stop there… The researchers have now developed an updated algorithm 10 times faster than the previous one, for household robot applications.

“If you just took the output of looking at it from one viewpoint, there’s a lot of stuff that might be missing, or it might be the angle of illumination or something blocking the object that causes a systematic error in the detector,” says Lawson Wong, a graduate student in electrical engineering and computer science and lead author on the new paper. “One way around that is just to move around and go to a different viewpoint.”

In order to find the new vision system for household robots, the researchers studied scenarios that contained 20 to 30 different images of household objects on a table, including duplicate images.

The researchers then identified an algorithm that “doesn’t discard any of the hypotheses it generates across successive images, but it doesn’t attempt to canvass them all, either.”

They added, “Suppose that the algorithm has identified three objects from one perspective and four from another. The most mathematically precise way to compare hypotheses would be to consider every possible set of matches between the two groups of objects: the set that matches objects 1, 2, and 3 in the first view to objects 1, 2, and 3 in the second; the set that matches objects 1, 2, and 3 in the first to objects 1, 2, and 4 in the second; the set that matches objects 1, 2, and 3 in the first view to objects 1, 3, and 4 in the second, and so on. In this case, if you include the possibilities that the detector has made an error and that some objects are occluded from some views, that approach would yield 304 different sets of matches.”

The findings from MIT were recently published in an edition of the International Journal of Robotics Research.

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