  Integration of Visual Inertial Odometry (VIO) methods into a modular control
system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of
cooperating UAVs in real-world conditions are presented in this paper.
Reliability analysis and fair performance comparison of several methods
integrated into a control pipeline for achieving full autonomy in real
conditions is provided. Although most VIO algorithms achieve excellent
localization precision and negligible drift on artificially created datasets,
the aspects of reliability in non-ideal situations, robustness to degraded
sensor data, and the effects of external disturbances and feedback control
coupling are not well studied. These imperfections, which are inherently
present in cases of real-world deployment of UAVs, negatively affect the
ability of the most used VIO approaches to output a sensible pose estimation.
We identify the conditions that are critical for a reliable flight under VIO
localization and propose workarounds and compensations for situations in which
such conditions cannot be achieved. The performance of the UAV system with
integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and
the ability to provide reliable pose estimation for the feedback control is
demonstrated onboard a UAV that is tracking dynamic trajectories under
challenging illumination.
