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

Improving drone localisation around wind turbines using monocular model-based tracking