Unlike other common mapping systems, such as simultaneous localization and mapping (SLAM), which are data intensive and difficult to maintain at real-time, the NanoMap uses depth-sensing to measure just the drone’s immediate surroundings. This enables the drone to understand generally where it is in relation to obstacles and anticipate how it will need to change course to avoid them.

“The key difference to previous work is that the researchers created a map consisting of a set of images with their position uncertainty rather than just a set of images and their positions and orientation,” says Sebastian Scherer, a systems scientist at Carnegie Mellon University’s Robotics Institute, wrote in an MIT release. “Keeping track of the uncertainty has the advantage of allowing the use of previous images even if the robot doesn’t know exactly where it is and allows in improved planning.”
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