  We present a method for simultaneous localisation and wind turbine model
fitting for a drone performing an automated surface inspection. We use a
skeletal parameterisation of the turbine that can be easily integrated into a
non-linear least squares optimiser, combined with a pose graph representation
of the drone's 3-D trajectory, allowing us to optimise both sets of parameters
simultaneously. Given images from an onboard camera, we use a CNN to infer
projections of the skeletal model, enabling correspondence constraints to be
established through a cost function. This is then coupled with GPS/IMU
measurements taken at key frames in the graph to allow successive optimisation
as the drone navigates around the turbine. We present two variants of the cost
function, one based on traditional 2D point correspondences and the other on
direct image interpolation within the inferred projections. Results from
experiments on simulated and real-world data show that simultaneous
optimisation provides improvements to localisation over only optimising the
pose and that combined use of both cost functions proves most effective.
