A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned “Magic Eye” image if they observe it in just the right way.
A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned “Magic Eye” image if they observe it in just the right way.
Robots typically “see” their environment through sensors that collect and translate a visual scene into a matrix of dots. Think of the world of, well, “The Matrix,” except that the 1s and 0s seen by the fictional character Neo are replaced by dots — lots of dots — whose patterns and densities outline the objects in a particular scene.
Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both.
With their new technique, the researchers say a robot can accurately pick out an object, such as a small animal, that is otherwise obscured within a dense cloud of dots, within seconds of receiving the visual data. The team says the technique can be used to improve a host of situations in which machine perception must be both speedy and accurate, including driverless cars and robotic assistants in the factory and the home.
Read more at Massachusetts Institute of Technology
Image: Robots currently attempt to identify objects in a point cloud by comparing a template object — a 3-D dot representation of an object, such as a rabbit — with a point cloud representation of the real world that may contain that object. CREDIT: Christine Daniloff, MIT
