Dataset Card for BorealTC

This dataset contains IMU and wheel timeseries of data recorded with a Husky A200 UGV. It was recorded on five different types of terrains: snow, ice, silty loam, asphalt, flooring.

Dataset Details

Dataset Description

Recorded with a Husky A200 wheeled UGV, BorealTC contains 116 min of Inertial Measurement Unit (IMU), motor current, and wheel odometry data, focusing on typical boreal forest terrains, notably snow, ice, and silty loam. The dataset also includes experiments on asphalt and flooring. All runs were recorded in Forêt Montmorency and on the main campus of Université Laval, Quebec City, Quebec, Canada.

• Curated by: Northern Robotics Laboratory, Université Laval, Québec, Canada
• License: CC0 1.0 Universal

Dataset Sources

• Repository: norlab-ulaval/BorealTC
• Paper: 10.1109/IROS58592.2024.10801407
• Page: BorealTC

Uses

This data was intended for terrain classification problems.

Direct Use

This dataset could be used as example data for sensor timeseries processing.

Dataset Structure

Each folder contains data for runs recorded on a specific terrain class. The data for each run is included in two CSV files: imu_*.csv and pro_*.csv:

borealtc
├── CLASS1
│   ├── imu_00.csv
│   ├── imu_01.csv
│   ├── ...
│   ├── pro_00.csv
│   ├── pro_01.csv
│   └── ...
└── CLASS2
    ├── imu_00.csv
    ├── imu_01.csv
    ├── ...
    ├── pro_00.csv
    ├── pro_01.csv
    └── ...

Each imu file contains IMU-recorded rotational velocities and linear accelerations.

time,wx,wy,wz,ax,ay,az
0.0,0.0015195721884953,0.0040130227245162,-0.0070785057037968,1.4258426214785636,-0.0832771308374386,9.609228803438713
...

Each pro file contains motor currents and wheel velocities recorded by the wheel service of the Husky.

time,curL,curR,velL,velR
0.0,1.78,2.57,0.0236220472440944,0.0236220472440944
...

Dataset Creation

Curation Rationale

This dataset aims at collecting terrain data on terrains typical of boreal forests.

Data Collection and Processing

This dataset was recorded with a Husky A200 wheeled UGV on five terrains.

Citation

BibTeX:

@inproceedings{LaRocque2024,
  title     = {{Proprioception Is All You Need: Terrain Classification for Boreal Forests}},
  url       = {http://dx.doi.org/10.1109/IROS58592.2024.10801407},
  doi       = {10.1109/iros58592.2024.10801407},
  booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  publisher = {IEEE},
  author    = {LaRocque,  Damien and Guimont-Martin,  William and Duclos,  David-Alexandre and Giguère,  Philippe and Pomerleau,  Fran\c{c}ois},
  year      = {2024},
  month     = oct,
  pages     = {11686–11693}
}

Contributions

Thanks to @WillGuimont and @Asers387 for the help in curating this dataset.