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Publication - Professor Andrew Calway

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

    Citation

    Moolan-Feroze, O, Karachalios, K, Nikolaidis, D & Calway, A, 2019, ‘Improving drone localisation around wind turbines using monocular model-based tracking’. in: 2019 IEEE International Conference on Robotics and Automation (ICRA 2019). Institute of Electrical and Electronics Engineers (IEEE), pp. 7713 - 7719

    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.

    Full details in the University publications repository