New algorithm could enable household robots to better identify objects in cluttered environments.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory believe that household robots should take advantage of their mobility and their relatively static environments to make object recognition easier, by imaging objects from multiple perspectives before making judgments about their identity. Matching up the objects depicted in the different images, however, poses its own computational challenges.
In a paper appearing in a forthcoming issue of the International Journal of Robotics Research, the MIT researchers show that a system using an off-the-shelf algorithm to aggregate different perspectives can recognize four times as many objects as one that uses a single perspective, while reducing the number of misidentifications.
They then present a new algorithm that is just as accurate but that, in some cases, is 10 times as fast, making it much more practical for real-time deployment with household robots.
“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.”
First stab
Wong and his thesis advisors — Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering, and Tomás Lozano-Pérez, the School of Engineering Professor of Teaching Excellence — considered scenarios in which they had 20 to 30 different images of household objects clustered together on a table. In several of the scenarios, the clusters included multiple instances of the same object, closely packed together, which makes the task of matching different perspectives more difficult.
Read more: Vision system for household robots
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