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.
@article{arxiv.1902.10474,
title = {Improving drone localisation around wind turbines using monocular model-based tracking},
author = {Oliver Moolan-Feroze and Konstantinos Karachalios and Dimitrios N. Nikolaidis and Andrew Calway},
journal= {arXiv preprint arXiv:1902.10474},
year = {2019}
}
Comments
Accepted at for the International Conference on Robotics and Automation