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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- feature-extraction |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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## Nordland Dataset |
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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/) |
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### Citation Information |
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Please cite the original publication if you use this dataset. |
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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. |
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```bibtex |
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@inproceedings{sunderhauf2013we, |
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title={Are we there yet? Challenging SeqSLAM on a 3000 km journey across all four seasons}, |
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author={S{\"u}nderhauf, Niko and Neubert, Peer and Protzel, Peter}, |
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booktitle={Proc. of workshop on long-term autonomy, IEEE international conference on robotics and automation (ICRA)}, |
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pages={2013}, |
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year={2013} |
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} |
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``` |
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### Dataset Description |
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The Nordland dataset captures a 728 km railway journey in Norway across four seasons: spring, summer, fall, and winter. |
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It is organised into four folders, each named after a season and containing 35,768 images. |
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These images maintain a one-to-one correspondence across folders. |
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For each traverse, the corresponding ground truth data is available in designated .csv files. |
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We have also included a file named `nordland_imageNames.txt`, which offers a filtered list of images. |
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This selection excludes segments captured when the train's speed fell below 15 km/h, as determined by the accompanying GPS data. |
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### Our Utilisation |
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We have used this dataset for the three publications below: |
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* Ensembles of Modular SNNs with/without sequence matching: [Applications of Spiking Neural Networks in Visual Place Recognition](https://arxiv.org/abs/2311.13186) |
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* 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) |
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* 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) |
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The code for our three papers mentioned above is publicly available at: [https://github.com/QVPR/VPRSNN](https://github.com/QVPR/VPRSNN) |
