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Season-invariant GNSS-denied visual localization for UAVs

This repository contains data and code for manuscript J. Kinnari, F. Verdoja, V. Kyrki "Season-invariant GNSS-denied visual localization for UAVs".

Setting up

Clone this repository on your computer.

Update on Nov 23, 2023: Pretrained checkpoint has been removed. Please contact author for acccess to it.

Install Anaconda. Create a new environment using the provided yml file:

conda env create -f environment.yml

Remark: The environment.yml file currently contains more packages than necessary.

Simple example

A simple example for computing similarity between two 96 by 96 pixel RGB images is provided in computeSimilarity.py. A few sample images can be found in folder sample_images. To check operation of the trained model with pairs of images, run

python3 computeSimilarity.py ../sample_images/sample1_anc.png ../sample_images/sample1_pos.png

Configuration

The file configs/configuration.yml contains various configuration details with comments for each parameter.

Training

After downloading the data to folder data as instructed at the end of this file, you may train the network yourself using the script trainSimilarityModel.py.

Citing

If this code is useful in your research, please consider citing:

@ARTICLE{9830867,
  author={Kinnari, Jouko and Verdoja, Francesco and Kyrki, Ville},
  journal={IEEE Robotics and Automation Letters}, 
  title={Season-Invariant GNSS-Denied Visual Localization for UAVs}, 
  year={2022},
  volume={7},
  number={4},
  pages={10232-10239},
  doi={10.1109/LRA.2022.3191038}}

Downloading data

What does one dataset contain?

Each dataset contains a number of aligned images, acquired at different times, from the same area, at a resolution of approximately 1 m/pixel.

Where to store images

Place each image in the corresponding folder under folder data or adjust paths in configuration.yml to match your configuration.

Acquiring Woodbridge and Fountainhead datasets

Rows 1 and 2 in the table below are from this paper by Goforth and Lucey.

Acquiring ge_area datasets

Rows 3 through 18 in the table below are acquired using Google Earth Pro desktop. Google Earth license does not allow distribution of data acquired with Google Earth Pro and therefore the dataset cannot be distributed as images directly. Also, Google Earth Pro does not appear to have a suitable API for exporting these kinds of images using a script.

However, in order to acquire the same dataset, you can download the same images with a bit of manual work according to these instructions:

  • Import each placemark file (.kmz) found in folder ge_placemarks into Google Earth Pro
  • Click View/Historical Imagery to enable historical imagery.
  • From Layers panel, remove tickmarks next to each layer selection.

For each placemark:

  • Double-click the placemark to center the view in the correct position, at the correct altitude
  • From Places tab, remove the tickmark for that placemark in order to remove it from view.
  • Slide the slider to one of the month/date combinations listed in table below.
  • Click the "Save Image" button. From Map Options, disable the items "Title and Description", "Legend", "Scale", "Compass" and "HTML Area"
  • From "Resolution", select "Maximum (4800 x 2987)"
  • Click "Save Image". Save the image with filename "<year>_<month>_<number>.jpg" in the folder defined in the table below.

These instructions were written for Google Earth Pro 7.3.4.8248 (64-bit) on Windows.

Listing of datasets

RowDataset nameImaging monthsDataset usePath
1woodbridge 2006/8 2008/8 2010/8 2013/8 2015/8 2017/8tra../data/USGS/sat_data/woodbridge/images
2fountainhead 2009/6 2012/5 2016/7tra../data/USGS/sat_data/fountainhead/images
3ge_area1 2011/3,4,6 2015/8,8 2018/5tra../data/google_earth_exports/area1
4ge_area2 2011/3,4,6 2013/8 2014/7 2015/8 2017/8 2018/5tra../data/google_earth_exports/area2
5ge_area4 2013/8 2014/7 2015/8 2017/7 2018/5,5 2020/3,3tra../data/google_earth_exports/area4
6ge_area5 2011/3 2013/3,8 2014/7 2015/8 2017/7,8 2018/5 2020/6tra../data/google_earth_exports/area5
7ge_area6 2002/7 2011/3 2013/3,8 2014/7 2015/8 2017/7 2018/5 2020/6tra../data/google_earth_exports/area6
8ge_area12 2013/4 2014/7 2017/7 2018/5 2020/6tra../data/google_earth_exports/area12
9ge_area13 2011/3,9 2012/5 2013/4 2020/3,6tra../data/google_earth_exports/area13
10ge_area14 2011/6 2012/5 2013/4,8 2015/8tra../data/google_earth_exports/area14
11ge_area15 2015/8 2016/7 2018/5 2020/4tra../data/google_earth_exports/area15
12ge_area3 2006/5 2008/4 2011/9 2012/5 2013/4,8 2014/5,7 2015/8 2017/7 2018/5,5 2020/3,3,6tes../data/google_earth_exports/area3
13ge_area3 2006/5 2008/4 2011/9 2012/5 2013/4,8 2014/5,7 2015/8 2017/7 2018/5,5 2020/3,3,6mcl../data/google_earth_exports/area3
14ge_area7 2002/7 2011/3 2013/3,8 2014/7 2015/8 2017/7 2018/5 2020/6mcl../data/google_earth_exports/area7
15ge_area8 2008/5 2010/7 2011/8 2013/4 2014/4 2017/5,8 2019/8 2020/3,6lik../data/google_earth_exports/area8
16ge_area9 2011/3,6 2017/8 2019/7 2021/7lik../data/google_earth_exports/area9
17ge_area10 2012/5 2014/8 2020/6 2021/4lik../data/google_earth_exports/area10
18ge_area11 2005/2 2013/6 2014/8 2019/4 2020/6 2021/4lik../data/google_earth_exports/area11

"Dataset use" column specifies the use of this dataset:

  • tra: used in training the network
  • tes: used in testing the network
  • mcl: used for Monte Carlo localization tests
  • lik: used for estimating likelihood from similarity score

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