Better robot vision

A neglected statistical tool could help robots better understand the objects in the world around them. Object recognition is one of the most widely studied problems in computer vision. But a robot that manipulates objects in the world needs to do more than just recognize them; it also needs to understand their orientation. Is that mug right-side up or upside-down? And which direction is its handle facing? To improve robots' ability to gauge object orientation, Jared Glover, a graduate student in MIT's Department of Electrical Engineering and Computer Science, is exploiting a statistical construct called the Bingham distribution. In a paper they're presenting in November at the International Conference on Intelligent Robots and Systems, Glover and MIT alumna Sanja Popovic '12, MEng '13, who is now at Google, describes a new robot-vision algorithm, based on the Bingham distribution, that is 15 percent better than its best competitor at identifying familiar objects in cluttered scenes. That algorithm, however, is for analyzing high-quality visual data in familiar settings. Because the Bingham distribution is a tool for reasoning probabilistically, it promises even greater advantages in contexts where information is patchy or unreliable.