In this video, we showcase aggressive autonomous drone races.
The drone is equipped with a pair of stereo cameras and a DJI N3 flight controller. All computing during the flight is done onboard. Our system consists of visual-inertial SLAM with loop closure, global mapping, local mapping, global trajectory optimization, local re-planning, and human-drone interaction interfaces.
Our system is built upon on a teach-and-repeat framework. A dense and globally consistent map is built before each experiment. In the teaching phase, the drone is piloted to provide a topological path (i.e. where hula hoop the drone should go through). In the repeating/execution phase, the drone converts the teaching path into a topologically equivalent optimal trajectory based on the known global obstacle map. The drone then executes the trajectory with user-expected velocity. The execution velocity can differ from that in the teaching phase. In fact, the drone operates much more aggressively during the execution phase thanks to the optimal trajectory generation. In the execution phase, state estimation and mapping functions maintain, such that the drone can avoid any new obstacles not identified in the global maps. Vision-based loop closure guarantees that the drone can operate in the same coordinate system for both the teaching and the repeating phase.
We target for better performance beyond human in challenging drone racing scenarios. Four video clips are presented to showcase the performance in indoor and outdoor, static and dynamic environments:
1. Indoor autonomous drone racing in a static environment
2. Indoor autonomous drone racing in an environment with unknown obstacles
3. Outdoor autonomous drone racing, trial 1
4. Outdoor autonomous drone racing, trial 2