Natural and Semi-Urban Dataset

A dataset designed to capture short-term environmental changes across both natural and semi-urban environments, with specific aim of analyzing how such dynamics impact localization and re-localization performance.

View the Project on GitHub RaoufDannaoui1/Natural_and_Semi-Urban_Dataset

Dataset overview

Weekly Multi-Temporal Dataset for Short-Term Localization and Environmental Change Analysis, designed to study the impact of real-world environmental changes on localization.

Overview

The dataset is a short-term, high-resolution, multi-modal dataset focused on understanding how real-world changes, such as vegetation growth, trimming, and object displacement, affect 3D LiDAR-based localization in dynamic outdoor environments.

The data was collected weekly from February to April 2025, across two contrasting outdoor scenarios:

This dataset provides a unique opportunity to analyze and benchmark localization robustness over short time intervals, making it ideal for applications in re-localization, change detection, SLAM, and dynamic mapping.

Data Content Description

Leica Pegasus TRK100 mounted on a Zoe electric vehicle

Acquisition Platform

All data is recorded using a Leica Pegasus TRK100 mounted on a Zoe electric vehicle, the Leica is equipped with:

Leica Pegasus TRK100 mounted on a Zoe electric vehicle

Folder Structure

weekXX_hhmm-DD-MM-YYYY/
      ├── assets/
      │   └── track_trajectories/
      ├── images_360/
      │   ├── SemiUrban_track1-2/
      │   └── Natural_track3-4/
      └── point_clouds/
          ├── SemiUrban_track1-2/
          └── Natural_track3-4/

Browse Dataset Files

Loading dataset files...

Citation

If you use this dataset in your research, please cite our paper.

@inproceedings{ardannaoui_2025_icp_analysis,
  title={When and Where Localization Fails: An Analysis of the Iterative Closest Point in Evolving Environments},
  author={Dannaoui, Abdelraouf and Laconte, Johann and Debain, Christophe and Pomerleau, Fran{\c{c}}ois and Checchin, Paul},
  booktitle={2025 European Conference on Mobile Robots (ECMR)},
  year={2025},
  organization={IEEE},
  note={Accepted for publication}
}

License

Data will be released under a permissive academic license (TBD).

Contact

For questions or collaboration inquiries, please contact:

Abdel-Raouf Dannaoui
Ph.D. Candidate in Robotics -- INRAE
dannaoui.abdelraouf@gmail.com