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

## TL;DR

This paper introduces a novel drone navigation method for wind turbine inspection that combines model-based tracking with neural network image analysis, significantly enhancing localization accuracy.

## Contribution

The paper presents a new approach integrating image-based measurements with GPS and IMU data for improved drone localization around wind turbines.

## Key findings

- Image measurements improve localization accuracy
- Model-based tracking applies to various turbine shapes
- Fusion of neural network output with sensor data enhances pose estimation

## 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.