  We present a novel method of integrating image-based measurements into a
drone navigation system for the automated inspection of wind turbines. We take
a model-based tracking approach, where a 3D skeleton representation of the
turbine is matched to the image data. Matching is based on comparing the
projection of the representation to that inferred from images using a
convolutional neural network. This enables us to find image correspondences
using a generic turbine model that can be applied to a wide range of turbine
shapes and sizes. To estimate 3D pose of the drone, we fuse the network output
with GPS and IMU measurements using a pose graph optimiser. Results illustrate
that the use of the image measurements significantly improves the accuracy of
the localisation over that obtained using GPS and IMU alone.
