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