Robots Learn To Understand Outdoor Scenes

Computer vision systems used by robots can now do a better job in understanding outdoor scenes, thanks to researchers at Carnegie Mellon University.


(Computers see outdoors)

“When people look at a photo, they understand that the scene is geometrically constrained,” said Abhinav Gupta, a post-doctoral fellow in CMU’s Robotics Institute. “We know that buildings aren’t infinitely thin, that most towers do not lean, and that heavy objects require support. It might not be possible to know the three-dimensional size and shape of all the objects in the photo, but we can narrow the possibilities. In the same way, if a computer can replicate an image, block by block, it can better understand the scene.”

This novel approach to automated scene analysis could eventually be used to understand not only the objects in a scene, but the spaces in between them and what might lie behind areas obscured by objects in the foreground, said Alexei A. Efros, associate professor of robotics and computer science at CMU. That level of detail would be important, for instance, if a robot needed to plan a route where it might walk, he noted.

In the new method devised by Gupta, Efros and Hebert, the image is first broken into various segments corresponding to objects in the image. Once the ground and sky are identified, other segments are assigned potential geometric shapes. The shapes also are categorized as light or heavy, depending on appearance; a surface that appears to be a brick wall, for instance, would be classified as heavy.

The computer then attempts to reconstruct the image using the virtual blocks. If a heavy block appears unsupported, the computer must substitute an appropriately shaped block, or make assumptions that the original block was obscured in the original image.

This capability is assumed by science fiction writers like Philip K. Dick, whose "leadies" from his 1964 novel The Penultimate Truth were robots that roamed the surface of the earth while most of humanity was kept underground.

From CMU.

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