  In recent years, Onboard Self Localization (OSL) methods based on cameras or
Lidar have achieved many significant progresses. However, some issues such as
estimation drift and feature-dependence still remain inherent limitations. On
the other hand, infrastructure-based methods can generally overcome these
issues, but at the expense of some installation cost. This poses an interesting
problem of how to effectively combine these methods, so as to achieve
localization with long-term consistency as well as flexibility compared to any
single method. To this end, we propose a comprehensive optimization-based
estimator for 15-dimensional state of an Unmanned Aerial Vehicle (UAV), fusing
data from an extensive set of sensors: inertial measurement units (IMUs),
Ultra-Wideband (UWB) ranging sensors, and multiple onboard Visual-Inertial and
Lidar odometry subsystems. In essence, a sliding window is used to formulate a
sequence of robot poses, where relative rotational and translational
constraints between these poses are observed in the IMU preintegration and OSL
observations, while orientation and position are coupled in body-offset UWB
range observations. An optimization-based approach is developed to estimate the
trajectory of the robot in this sliding window. We evaluate the performance of
the proposed scheme in multiple scenarios, including experiments on public
datasets, high-fidelity graphical-physical simulator, and field-collected data
from UAV flight tests. The result demonstrates that our integrated localization
method can effectively resolve the drift issue, while incurring minimal
installation requirements.
