A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data. 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. The technology was developed by Dr. Carlone, assistant professor of aeronautics and astronautics and a member of MIT’s Laboratory for Information and Decision Systems (LIDS) and graduate student Heng Yang. 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. The details of the technique will be presented at the Conference of Robotics: Science and Systems, June 22-26, 2019, in Germany.
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. The template image includes “features,” or collections of dots that indicate characteristic curvatures or angles of that object, such the bunny’s ear or tail. Existing algorithms first extract similar features from the real-life point cloud, then attempt to match those features and the template’s features, and ultimately rotate and align the features to the template to determine if the point cloud contains the object in question. But the point cloud data that streams into a robot’s sensor invariably includes errors, in the form of dots that are in the wrong position or incorrectly spaced, which can significantly confuse the process of feature extraction and matching. As a consequence, robots can make a huge number of wrong associations, or what researchers call “outliers” between point clouds, and ultimately misidentify objects or miss them entirely.
Yang and Carlone instead devised a technique that prunes away outliers in “polynomial time,” meaning that it can do so quickly, even for increasingly dense clouds of dots. The technique can thus quickly and accurately identify objects hidden in cluttered scenes. The team developed an “adaptive voting scheme” algorithm to prune outliers and match an object’s size and position. The researchers developed a separate algorithm for rotation, which finds the orientation of the template object in three-dimensional space. “Navigation, collaborative manufacturing, domestic robots, search and rescue, and self-driving cars is where we hope to make an impact,” Carlone says. This research was supported in part by the Army Research Laboratory, the Office of Naval Research, and the Google Daydream Research Program.
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