Quadrotors are amongst the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential due to their inherent smoothness.  Recent works resorted to numerical optimization, but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time-allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor's actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time-allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world's largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.

This data contains drone flight tracks of two professional human pilots and a proposed planning approach, flown in a drone racing environment in one of the worlds largest flight arenas equipped with a VICON motion capture system.

All data is provided as is and can be evaluated using the included analysis scripts.