  In this paper, we tackle the problem of flying a quadrotor using time-optimal
control policies that can be replanned online when the environment changes or
when encountering unknown disturbances. This problem is challenging as the
time-optimal trajectories that consider the full quadrotor dynamics are
computationally expensive to generate (order of minutes or even hours). We
introduce a sampling-based method for efficient generation of time-optimal
paths of a point-mass model. These paths are then tracked using a Model
Predictive Contouring Control approach that considers the full quadrotor
dynamics and the single rotor thrust limits. Our combined approach is able to
run in real-time, being the first time-optimal method that is able to adapt to
changes on-the-fly. We showcase our approach's adaption capabilities by flying
a quadrotor at more than 60 km/h in a racing track where gates are moving.
Additionally, we show that our online replanning approach can cope with strong
disturbances caused by winds of up to 68 km/h.
