Datasets:
Somayeh-h
Added the dataset imageNames, the .csv ground truth files, code to check the dataset, and the readme file.
0ade8fd
license: cc-by-nc-sa-4.0 | |
## Nordland dataset | |
This dataset is from the original videos released here: [https://nrkbeta.no/2013/01/15/nordlandsbanen-minute-by-minute-season-by-season/](https://nrkbeta.no/2013/01/15/nordlandsbanen-minute-by-minute-season-by-season/) | |
### Citation Information | |
Please cite the original publication if you use this dataset. | |
Sünderhauf, Niko, Peer Neubert, and Peter Protzel. "Are we there yet? Challenging SeqSLAM on a 3000 km journey across all four seasons." Proc. of Workshop on Long-Term Autonomy, IEEE International Conference on Robotics and Automation (ICRA). 2013. | |
```bibtex | |
@inproceedings{sunderhauf2013we, | |
title={Are we there yet? Challenging SeqSLAM on a 3000 km journey across all four seasons}, | |
author={S{\"u}nderhauf, Niko and Neubert, Peer and Protzel, Peter}, | |
booktitle={Proc. of workshop on long-term autonomy, IEEE international conference on robotics and automation (ICRA)}, | |
pages={2013}, | |
year={2013} | |
} | |
``` | |
### Dataset Description | |
The Nordland dataset captures a 728 km railway journey in Norway across four seasons: spring, summer, fall, and winter. | |
It is organized into four folders, each named after a season and containing 35,768 images. | |
These images maintain a one-to-one correspondence across folders. | |
For each traverse, the corresponding ground truth data is available in designated .csv files. | |
We have also included a file named `nordland_imageNames.txt`, which offers a filtered list of images. | |
This selection excludes segments captured when the train's speed fell below 15 km/h, as determined by the accompanying GPS data. | |
### Our utilisation | |
We have used this dataset for the three publications below: | |
* Ensembles of Modular SNNs with/without sequence matching: [Applications of Spiking Neural Networks in Visual Place Recognition](https://arxiv.org/abs/2311.13186) | |
* Modular SNN: [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition (ICRA 2023)](https://arxiv.org/abs/2209.08723) DOI: [10.1109/ICRA48891.2023.10160749](https://doi.org/10.1109/ICRA48891.2023.10160749) | |
* Non-modular SNN: [Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments (RAL + ICRA2022)](https://arxiv.org/abs/2109.06452) DOI: [10.1109/LRA.2022.3149030](https://doi.org/10.1109/LRA.2022.3149030) | |
The code for our three papers mentioned above is publicly available at: [https://github.com/QVPR/VPRSNN](https://github.com/QVPR/VPRSNN) | |