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arXiv · 1902.10474

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

Oliver Moolan-Feroze·Konstantinos Karachalios·Dimitrios N. Nikolaidis·Andrew Calway
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Abstract

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.

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Oliver Moolan-Feroze, Konstantinos Karachalios, Dimitrios N. Nikolaidis, Andrew Calway. 2019-02-27. Improving drone localisation around wind turbines using monocular model-based tracking. https://arxiv.org/abs/1902.10474

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