HKUST Aerial Robotics Group

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: Video1, Video2
Code: https://github.com/HKUST-Aerial-Robotics/Teach-Repeat-Replan

multiple sensors support (stereo cameras / mono camera+IMU / stereo cameras+IMU)
online spatial calibration (transformation between camera and IMU)
online temporal calibration (time offset between camera and IMU)
visual loop closure.

We are the TOP open-sourced stereo algorithm on KITTI Odometry Benchmark by 12 Jan. 2019.

HKUST Aerial Robotics Group

Code for Autonomous Drone Race is now available on GitHub

Autonomous drone racing

Autonomous drone racing

Code for VINS-Fusion is now available on GitHub

VINS-Fusion