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.
The team's new algorithm is 10 times as fast, making it much more practical for real-time deployment with household robots.
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