README.md · Somayeh-h/Nordland at main

metadata

license: cc-by-nc-sa-4.0
size_categories:
  - 100K<n<1M

Nordland Dataset

This dataset is from the original videos released here: 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.

@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 organised 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
Modular SNN: Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition (ICRA 2023) DOI: 10.1109/ICRA48891.2023.10160749
Non-modular SNN: Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments (RAL + ICRA2022) DOI: 10.1109/LRA.2022.3149030

The code for our three papers mentioned above is publicly available at: https://github.com/QVPR/VPRSNN