Modeling Uncertainty Helps MIT's Drone Zip Around Obstacles

It's not too hard to make a drone that can fly very fast, and it's not too hard to make a drone that can avoid obstacles. Making a drone that can do both at once is much more difficult, but it's necessary in order for them to be real-world useful. At MIT CSAIL, Pete Florence (in Russ Tedrake's lab) has developed a new motion planning framework called NanoMap, which uses a sequence of 3D snapshots to allow fast-moving (10 m/s) drones to safely navigate around obstacles even if they're not entirely sure where they are. Don't worry if you don't catch all the details, as we'll take a crack at explaining what's going on afterwards: I don't mind telling you, this is one of those papers which reminds me that I have a degree in geology rather than robotics. So, let's start with the key idea of NanoMap, which the paper helpfully makes explicit right there in the abstract: The key idea of NanoMap is to store a history of noisy relative pose transforms and search over a corresponding set of depth sensor measurements for the minimum-uncertainty view of a queried point in space.