ardannaoui/Natural_and_SemiUrban_Dataset

Natural and Semi-Urban Dataset

Weekly Multi-Temporal Dataset for Short-Term Localization and Environmental Change Analysis

Dataset website: raoufdannaoui1.github.io/Natural_and_Semi-Urban_Dataset/

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:

• Natural Environment: INRAE Forest, Clermont-Ferrand
• Semi-Urban Environment: Around Université Clermont Auvergne

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.

Dataset Description

Each weekly session contains:

• Two Environments:

  • Track 1-2 (bidirectional): Semi-urban university campus (~850m)
  • Track 3-4 (bidirectional): Natural forest path (~680m)

• Data Structure (per week):

  weekXX_hhmm-DD-MM-YYYY/
  │── assets/
  │   └── track_trajectories
  │
  │── images_360/
  │   ├── SemiUrban_track1-2/
  │   └── Urban_track3-4/
  │
  └── point_clouds/
      ├── SemiUrban_track1-2/
      └── Urban_track3-4/
  

• Contents:

  • Colorized & classified point clouds
    • Provided per semantic class (ground, vegetation, vehicles, etc.)
    • ~100 million points for a Semi-Urban map and ~150 million points for a Natural map
    • Average size: ~4 GB for a Semi-Urban map and ~5.5 GB for a Natural map (LAS format files)
  • Panoramic images
    • 360° images captured every 3 meters along each track
    • Resolution: 7040 × 3520 pixels
    • Average size: ~2–3 MB per image (JPEG)
    • Includes GNSS-tagged camera trajectory
  • GNSS/IMU trajectories
    • Provides full pose: position (x, y, z) and orientation (quaternion: qw, qx, qy, qz)

Applications

This dataset supports research in:

• Re-localization and map alignment
• Short-term environmental dynamics
• ICP evaluation (Point-to-Point, Point-to-Plane, etc.)
• Scene segmentation and change detection
• Structure-aware risk prediction in navigation

Acquisition Platform

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

• Two 16-beam LiDARs
• 360° panoramic camera
• GNSS and IMU for centimeter-level pose accuracy

Data Availability

The full dataset files (point clouds, images, and trajectories) is publicly available directly on Hugging Face, or via raoufdannaoui1.github.io/Natural_and_Semi-Urban_Dataset for selective download.

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}
 }

Contact

For questions or collaborations, feel free to contact:

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