For a list of all papers click here

Wednesday, 23 April 2014

Learning to Predict Obstacle Aerodynamics from Depth Images for Micro Air Vehicles

This work develops a method to anticipate the aerodynamic ground effects a Micro Air Vehicle will have when going above different obstacles. This works by learning a mapping from depth images to the acceleration experienced from flying above a variety of objects. Computing full 3D aerodynamic effects using air flow simulation onboard a MAV is currently unfeasible. This work uses the alternative approach of learning the visual appearance of objects that produce a given effect which therefore turns the problem into one of regression rather than raw computation.
With the current "easiness" with which 3D maps are now possible to be constructed, this work in a way aims to enhance maps with information that is beyond purely geometric. We have also closed the control loop so we correct for the deviation in anticipation, but that is for another paper.

  • John Bartholomew, Andrew Calway and Walterio Mayol-Cuevas, Learning to Predict Obstacle Aerodynamics from Depth Images for Micro Air Vehicles by , IEEE ICRA 2014. [PDF]