A statistical tool can improve 'vision' in robots by helping them better understand the objects in the world around them.
Object recognition is one of the most widely studied problems in computer vision, researchers said.
To
improve robots' ability to gauge object orientation, Jared Glover, a
graduate student in Massachusetts Institute of Technology (MIT)'s
Department of Electrical Engineering and Computer Science, is exploiting
a statistical construct called the Bingham distribution.
In
a paper to be presented at the International Conference on Intelligent
Robots and Systems, Glover and MIT alumna Sanja Popovic, who is now at
Google, describes a new robot-vision algorithm, based on the Bingham
distribution, that is 15% better than its best competitor at identifying
familiar objects in cluttered scenes.
That algorithm, however, is for analysing 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.
In cases where visual
information is particularly poor, the algorithm offers an improvement of
more than 50% over the best alternatives.
"Alignment is key to many problems in robotics, from object-detection and tracking to mapping," Glover said.
"And
ambiguity is really the central challenge to getting good alignments in
highly cluttered scenes, like inside a refrigerator or in a drawer.
That's why the Bingham distribution seems to be a useful tool, because
it allows the algorithm to get more information out of each ambiguous,
local feature," Glover said.
One reason the
Bingham distribution is so useful for robot vision is that it provides a
way to combine information from different sources, researchers said.
Determining
an object's orientation entails trying to superimpose a geometric model
of the object over visual data captured by a camera -- in the case of
Glover's work, a Microsoft Kinect camera, which captures a 2D colour
image together with information about the distance of the colour
patches.
In experiments involving visual data
about particularly cluttered scenes - depicting the kinds of
environments in which a household robot would operate - Glover's
algorithm had about the same false-positive rate as the best existing
algorithm: About 84% of its object identifications were correct, versus
83% for the competition.
But it was able to identify a significantly higher percentage of the objects in the scenes - 73% versus 64%.
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