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https://github.com/NeBula-Autonomy/nebula-odometry-dataset

Ready to test your SLAM system in challenging datasets from extreme environments? Try this out! The dataset is provided by the Team CoSTAR that has been intensively testing multi-robot systems in real world environments such as caves, tunnels, abandoned factories and industrial plants for the DARPA Subterranean Challenge.
https://github.com/NeBula-Autonomy/nebula-odometry-dataset

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Ready to test your SLAM system in challenging datasets from extreme environments? Try this out! The dataset is provided by the Team CoSTAR that has been intensively testing multi-robot systems in real world environments such as caves, tunnels, abandoned factories and industrial plants for the DARPA Subterranean Challenge.

Lists

README

        


drawing

# NeBula odometry dataset

- [Overview](pages/overview.md)
- [Download datasets](pages/dataset.md)
- Topics and frame convention
- [LiDAR dataset](pages/topic_lidar.md)
- [Hovermap dataset](pages/topic_hovermap.md)

drawing

# Cite

If you use this dataset in your research, we would kindly ask that you cite the following publication:

```
@ARTICLE{reinke2022iros,
author={Reinke, Andrzej and Palieri, Matteo and Morrell, Benjamin and Chang, Yun and Ebadi, Kamak and Carlone, Luca and Agha-Mohammadi, Ali-Akbar},
journal={IEEE Robotics and Automation Letters},
title={LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping},
year={2022},
volume={},
number={},
pages={1-8},
doi={10.1109/LRA.2022.3181357}}
```

Paper available on arXiv [here](https://arxiv.org/abs/2205.11784)

# Problems or clarification needed?
Submit ticket [here](https://github.com/NeBula-Autonomy/nebula-odometry-dataset/issues)