  Traditionally, controllers and state estimators in robotic systems are
designed independently. Controllers are often designed assuming perfect state
estimation. However, state estimation methods such as Visual Inertial Odometry
(VIO) drift over time and can cause the system to misbehave. While state
estimation error can be corrected with the aid of GPS or motion capture, these
complementary sensors are not always available or reliable. Recent work has
shown that this issue can be dealt with by synthesizing robust controllers
using a data-driven characterization of the perception error, and can bound the
system's response to state estimation error using a robustness constraint. We
investigate the application of this robust perception-based approach to a
quadrotor model using VIO for state estimation and demonstrate the benefits and
drawbacks of using this technique in simulation and hardware. Additionally, to
make tuning easier, we introduce a new cost function to use in the control
synthesis which allows one to take an existing controller and "robustify" it.
To the best of our knowledge, this is the first robust perception-based
controller implemented in real hardware, as well as one utilizing a data-driven
perception model. We believe this as an important step towards safe, robust
robots that explicitly account for the inherent dependence between perception
and control.
