  The development of safe and reliable autonomous unmanned aerial vehicles
relies on the ability of the system to recognise and adapt to changes in the
local environment based on sensor inputs. State-of-the-art local tracking and
trajectory planning are typically performed using camera sensor input to the
flight control algorithm, but the extent to which environmental disturbances
like rain affect the performance of these systems is largely unknown. In this
paper, we first describe the development of an open dataset comprising ~335k
images to examine these effects for seven different classes of precipitation
conditions and show that a worst-case average tracking error of 1.5 m is
possible for a state-of-the-art visual odometry system (VINS-Fusion). We then
use the dataset to train a set of deep neural network models suited to mobile
and constrained deployment scenarios to determine the extent to which it may be
possible to efficiently and accurately classify these `rainy' conditions. The
most lightweight of these models (MobileNetV3 small) can achieve an accuracy of
90% with a memory footprint of just 1.28 MB and a frame rate of 93 FPS, which
is suitable for deployment in resource-constrained and latency-sensitive
systems. We demonstrate a classification latency in the order of milliseconds
using typical flight computer hardware. Accordingly, such a model can feed into
the disturbance estimation component of an autonomous flight controller. In
addition, data from unmanned aerial vehicles with the ability to accurately
determine environmental conditions in real time may contribute to developing
more granular timely localised weather forecasting.
