  Unmanned aerial vehicles (UAVs) have increasingly been adopted for safety,
security, and rescue missions, for which they need precise and reliable pose
estimates relative to their environment. To ensure mission safety when relying
on visual perception, it is essential to have an approach to assess the
integrity of the visual localization solution. However, to the best of our
knowledge, such an approach does not exist for optimization-based visual
localization. Receiver autonomous integrity monitoring (RAIM) has been widely
used in global navigation satellite systems (GNSS) applications such as
automated aircraft landing. In this paper, we propose a novel approach inspired
by RAIM to monitor the integrity of optimization-based visual localization and
calculate the protection level of a state estimate, i.e. the largest possible
translational error in each direction. We also propose a metric that
quantitatively evaluates the performance of the error bounds. Finally, we
validate the protection level using the EuRoC dataset and demonstrate that the
proposed protection level provides a significantly more reliable bound than the
commonly used $3\sigma$ method.
