Improving drone localisation around wind turbines using monocular model-based tracking
This addresses the problem of precise drone navigation for automated wind turbine inspection, which is an incremental improvement in domain-specific robotics.
The paper tackled drone localization for wind turbine inspection by integrating image-based measurements with GPS/IMU data using a model-based tracking approach, resulting in significantly improved accuracy over GPS/IMU alone.
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.