2019/02/27 by Oliver Moolan-Feroze, Konstantinos Karachalios, Moolan-Feroze, Oliver +6
Computer Science · Engineering · #Advanced Neural Network Applications #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #UAV Applications and Optimization #cs.RO
paper · pdf · doi:10.48550/arxiv.1902.10474
Accepted at for the International Conference on Robotics and Automation
arxiv created 2019/02/27 · openalex publication_date 2019/02/27 · arxiv updated 2019/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.