Code for Autonomous Drone Race is now available on GitHub

We release Teach-Repeat-Replan, which is a complete and robust system enables Autonomous Drone Race.

Teach-Repeat-Replan can be applied to situations where the user has a preferable rough route but isn’t able to pilot the drone ideally, such as drone racing. With our system, the human pilot can virtually control the drone with his/her navie operations, then our system automatically generates a very efficient repeating trajectory and autonomously execute it. During the flight, unexpected collisions are avoided by onboard sensing/replanning. Teach-Repeat-Replan can also be used for normal autonomous navigations. For these applications, a drone can autonomously fly in complex environments using only onboard sensing and planning.

Major components are:

  • Planning: flight corridor generation, global spatial-temporal planning, local online re-planning
  • Perception: global deformable surfel mapping, local online ESDF mapping
  • Localization: global pose graph optimization, local visual-inertial fusion
  • Controlling: geometric controller on SE(3)

Authors: Fei Gao, Boyu Zhou, and Shaojie Shen

Videos:  Video1Video2

Code: https://github.com/HKUST-Aerial-Robotics/Teach-Repeat-Replan