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Robots and Dense Point Clouds

New Approach Quickly Finds Hidden Objects in Dense Point Clouds

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 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. 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.

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