AG-LOAM Dataset

Description

AG-LOAM dataset has been released to facilitate the evaluation of LiDAR-based odometry algorithms in agricultural environments.

1. It was collected by a wheeled mobile robot at the Agricultural Experimental Station of the University of California, Riverside, during Winter 2022 and Winter 2023.
2. It provides LiDAR point cloud data captured using a Velodyne VLP-16 sensor, along with ground-truth trajectories obtained from an RTK-GPS system.
3. It consists of 18 sequences collected over three phases, covering diverse planting environments, terrain conditions, path patterns, and robot motion profiles.
4. It spans a total operation time of 3 hours, covers a total distance of 7.5 km, and constitutes 150 GB of data.

Update Frequency

NA

License

Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Documentation

https://github.com/UCR-Robotics/AG-LOAM

Managed By

Autonomous Robots and Control Systems Lab

See all datasets managed by Autonomous Robots and Control Systems Lab.

Contact

Hanzhe Teng (hteng007@ucr.edu), Konstantinos Karydis (kkarydis@ece.ucr.edu)

How to Cite

AG-LOAM Dataset was accessed on DATE from https://registry.opendata.aws/ag-loam.

Usage Examples

Tools & Applications

• Source code of the LiDAR-only odometry and mapping system by Hanzhe Teng et al.
  

Publications

• Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms by Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis
  
• Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms by Hanzhe Teng, Yipeng Wang, Dimitrios Chatziparaschis, Konstantinos Karydis