Datasets:
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/
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 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
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