https://github.com/qvpr/predict2improve
https://github.com/qvpr/predict2improve
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/qvpr/predict2improve
- Owner: QVPR
- License: mit
- Created: 2022-07-07T01:18:27.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-10-26T22:28:40.000Z (over 3 years ago)
- Last Synced: 2023-03-10T03:47:37.395Z (over 3 years ago)
- Language: Jupyter Notebook
- Size: 1010 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Predicting to Improve: Integrity Measures for Assessing Visual Localization Performance
This repo contains source code for our paper: "Predicting to Improve: Integrity Measures for Assessing Visual Localization Performance", available from the [publisher](https://ieeexplore.ieee.org/document/9830823) and on QUT [ePrints](https://eprints.qut.edu.au/234489/).
## Attribution
When using code within this repository, please reference the following [paper](https://ieeexplore.ieee.org/document/9830823):
```
@ARTICLE{9830823,
author={Carson, Helen and Ford, Jason J. and Milford, Michael},
journal={IEEE Robotics and Automation Letters},
title={Predicting to Improve: Integrity Measures for Assessing Visual Localization Performance},
year={2022},
volume={7},
number={4},
pages={9627-9634},
doi={10.1109/LRA.2022.3191205}}
```
## Installation
We recommend using conda or mamba to install all dependencies. Mamba can be installed from [`mambaforge`](https://github.com/conda-forge/miniforge).
```bash
conda create --name vpred_env python=3.9 numpy matplotlib jupyterlab scikit-learn -c conda-forge
conda activate vpred_env
```
Download the example Nordland feature set using the link [here](https://cloudstor.aarnet.edu.au/plus/s/UgpN69h5VP2thG8).
Note these features are derived from the partitioned Nordland testset published at http://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset by David Olid et al in **Single-View Place Recognition under Seasonal Changes** *In PPNIV Workshop at IROS 2018*.
Run the jupyterlab example notebook using:
```
jupyter lab example.ipynb
```
## Licence
The code is licensed under the [MIT License](./LICENSE).