Enhancing Visual Place Recognition in Spatial Domain on Aerial Vehicle Platforms.
Aerial localisation |
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- torch
- torchvision
- opencv-python
- tensorboard
- matplotlib
- sklearn
- numpy
Please see "requirements.txt" for a detailed list of packages and versions.
experiment\
├── features
| ├── Beijing
| | ├── Beijing_feature_GeoCluser_1.00.npy
| | ├── Beijing_feature_GeoCluser_0.85.npy
| | ├── Beijing_feature_GeoCluser_0.85.npy
├── map
| ├── Beijing.tif
├── query_images
| ├── Beijing_query
| | ├── 0.png
| | ├── 1.png
├── route
| ├──Beijing_route.npy
├── tiles
| ├── Beijing_ref_scale_0.85
| | ├── 0.png
| | ├── 1.png
| ├── Beijing_ref_scale_1.00
| | ├── 0.png
| | ├── 1.png
| ├── Beijing_ref_scale_1.35
| | ├── 0.png
| | ├── 1.png
Note: scale_1.00, scale_0.85, scale_1.35 refer to the scale of the map tile image and the camera image.
If there is no database, put the database image into experiment/tiles/:
python main.py --model=GeoCluster --mode=Pre
If the database already exists:
python main.py --model=GeoCluster --mode=PF
When using code within this repository, please refer the following paper in your publications:
@ARTICLE{10423811,
author={Chen, Chao and He, Mengfan and Wang, Jun and Meng, Ziyang},
journal={IEEE Robotics and Automation Letters},
title={GeoCluster: Enhancing Visual Place Recognition in Spatial Domain on Aerial Vehicle Platforms},
year={2024},
volume={9},
number={3},
pages={3013-3020},
keywords={Feature extraction;Visualization;Task analysis;Training;Location awareness;Databases;Autonomous aerial vehicles;Vision-based navigation;localization;recognition;deep learning for visual perception},
doi={10.1109/LRA.2024.3363536}}