Nowadays, Unmanned Aerial Vehicles (UAVs) navigating outdoors rely heavily on GPS for localisation and autonomous flight or applications for aerial photography recording with a GPS coordinate. However, GPS may fail or become unreliable, thus compromising the flight mission. Motivated by this scenario, in this work, we present a study on the use of popular Convolutional Neural Networks (CNN) to address the problem of geo-localisation from a single aerial image. We compare CNN-based architectures from the state of the art, and introduce a compact architecture to speed up the inference process without affecting the estimation error. For our experiments, aerial images were recorded with a monocular camera on board a UAV, flying outdoors with a height between 20 to 25 metres. On average, our compact network achieves a minimum estimation error of 2.8 metres and a maximum of 6.1 metres, which is comparable to the performance of other networks in the state of the art. However, our network achieves on average an operation frequency of 103 fps versus 69 fps achieved by the fastest network in the comparison analysis. These results are promising since such speed would enable fast geo-localisation with cameras capturing images at those frame rates, which are useful for obtaining neater images than with conventional cameras working at 30 fps.
It consists of 2 steps: 1) Real-time image acquisition using the drone’s onboard camera. 2) GPS coordinates estimation using a compact convolutional neural network (CNN) based on PoseNet called CompactPN (Compact PoseNet).
The architecture consists of 4 convolutional layers, 3 Inception modules, 1 fully-connected layers with the output layer separated into three neurons, one for each coordinate after the pooling layer.
A video of this approach can be watched at Youtube
- Ubuntu 16.04
- ROS kinetic Kame
- Python 2.7.15
- PyTorch 1.1.0
- Cuda 9.0
- Cudnn 7.3.0
If you use any of data or code, please cite the following reference:
Cabrera-Ponce, A. A., & Martinez-Carranza, J. "Convolutional neural networks for geo-localisation with a single aerial image", Journal of Real-Time Image Processing, 2022, 19(3), 565-575. https://doi.org/10.1007/s11554-022-01207-1
@article{cabrera2022convolutional,
title={Convolutional Neural Networks for Geo-Localisation with a Single Aerial Image},
author={Cabrera-Ponce, Aldrich A and Martinez-Carranza, Jose},
journal={Journal of Real-Time Image Processing},
volume={19},
number={3},
pages={565--575},
year={2022},
publisher={Springer}
}
- Cabrera-Ponce, A. A., & Martinez-Carranza, J. "Aerial geo-localisation for MAVs using PoseNet," 2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), Cranfield, United Kingdom, 2019, pp. 192-198, https://doi.org/10.1109/REDUAS47371.2019.8999713
@inproceedings{cabrera2019aerial,
title={Aerial geo-localisation for MAVs using PoseNet},
author={Cabrera-Ponce, Aldrich A and Martinez-Carranza, J},
booktitle={2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS)},
pages={192--198},
year={2019},
organization={IEEE}
}
We are thankful for the processing time granted by the National Laboratory of Supercomputing (LNS) under the project 201902063C. The first author is thankful to Consejo Nacional de Ciencia y Tecnología (CONACYT) for the scholarship No. 727018.