This repo covers the supplementary materials of the following paper:
Two parts of the dataset can be downloaded from the Tsinghua Cloud or Baidu Netdisk, including the captured frames and satellite imageries for training.
- Flight data covering about 70 km trajectories with various terrains, multiple heights and illumination changes.
- For sequence-based visual localization, 11 frame sequences with different paths ranging from the shortest one of 3.7 km to the longest up to 11 km.
- For visual place recognition, 18361 separate aerial-based images with 14096 cropped corresponding map patches are provided.
RGB camera (FLIR BFS-U3-31S4C-C) is attached with a gimbal to the UAV. The GNSS (NovAtel OEM718D) has 1.5 m accuracy in RMS with the single point mode.
In the sequence-based visual localization part, we have prepared the evaluation data as follows.
+--- geo_referenced_map
| +--- @[email protected]@[email protected]@[email protected]
| +--- @[email protected]@[email protected]@[email protected]
+--- long_trajtr
| +--- 2023-03-16-18-04-01
| +--- 2023-03-18-12-18-25
| +--- 2023-03-18-12-47-05
| +--- 2023-03-18-14-38-32
| +--- 2023-03-18-15-01-14
| +--- 2023-03-18-15-40-18
+--- short_trajtr
| +--- 2023-03-11-11-48-35
| +--- 2023-03-16-16-58-43
| +--- 2023-03-18-16-30-27
| +--- 2023-03-18-16-43-16
| +--- 2023-03-18-16-55-37
There are two geo-referenced map with different size for flight sequences with different length. They are also renamed as the the following formats (maps are heading north):
@map_name@LeftTopLongitude@LeftTopLatitude@RightBottomLongitude@[email protected]
And the captured frames are also re-organized as @UTCTimeStamp@Longitude@[email protected] (frames are heading east).
The visual place recognition part are presented as follows:
+--- map_database
| +--- level_1
| +--- level_2
| +--- level_3
+--- query_images
| +--- query_images_1
| +--- query_images_2
| +--- query_images_3
| +--- query_images_4
+--- raw_satellite_imagery
| +--- @[email protected]@[email protected]@[email protected]
The map tiles in the map_database folder are sampled from the satellite imagery, which is downloaded from the Google Earth. The different levels in the map_database present the tiles with different size. These tiles are re-organized as follows (tiles are heading east to be consistent with the captured frame):
@map@LeftBottomLongitude@LeftBottomLatitude@RightTopLongitude@[email protected]
It is worth mentioning that the definition here is different from the VAL part because we adjust the heading of these tiles to make the VPR task more easier.
The query_imagefolder contains the capture four parts of captured frames with names as @Longitude@[email protected].
We have also provided a lot of satellite imageries collected from different years using USGS. As the training data, these imageries can help you to get a new VPR model designed for the aerial-based platform.
If you find this dataset useful for your research, please consider citing the paper
@article{he2024aerialvl,
author={He, Mengfan and Chen, Chao and Liu, Jiacheng and Li, Chunyu and Lyu, Xu and Huang, Guoquan and Meng, Ziyang},
journal={IEEE Robotics and Automation Letters},
title={AerialVL: A Dataset, Baseline and Algorithm Framework for Aerial-Based Visual Localization With Reference Map},
year={2024},
volume={9},
number={10},
pages={8210-8217},
publisher={IEEE}
}