For household robots ever to be practical, they’ll need to be able to recognize the objects they’re supposed to manipulate. But while object recognition is one of the most widely studied topics in artificial intelligence, even the best object detectors still fail much of the time.
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.
Read more at MIT News as PhD student and lead author on the new paper, Lawson Wong, talks about the project. Photo by John Boyd.