  Fault-tolerant control is crucial for safety-critical systems, such as
quadrotors. State-of-art flight controllers can stabilize and control a
quadrotor even when subjected to the complete loss of a rotor. However, these
methods rely on external sensors, such as GPS or motion capture systems, for
state estimation. To the best of our knowledge, this has not yet been achieved
with only onboard sensors. In this paper, we propose the first algorithm that
combines fault-tolerant control and onboard vision-based state estimation to
achieve position control of a quadrotor subjected to complete failure of one
rotor. Experimental validations show that our approach is able to accurately
control the position of a quadrotor during a motor failure scenario, without
the aid of any external sensors. The primary challenge to vision-based state
estimation stems from the inevitable high-speed yaw rotation (over 20 rad/s) of
the damaged quadrotor, causing motion blur to cameras, which is detrimental to
visual inertial odometry (VIO). We compare two types of visual inputs to the
vision-based state estimation algorithm: standard frames and events.
Experimental results show the advantage of using an event camera especially in
low light environments due to its inherent high dynamic range and high temporal
resolution. We believe that our approach will render autonomous quadrotors
safer in both GPS denied or degraded environments. We release both our
controller and VIO algorithm open source.
