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Purdue ACRE Cornfield Dataset

This dataset, collected with Purdue-AgBot (P-AgBot) at the Agronomy Center for Research and Education (ACRE) during Summer 2023, supports research in autonomous navigation and crop monitoring in cornfields.

ACRE image

ACRE video

Overview

  • Sensor Data: Includes 3D LiDAR, IMU, wheel encoders, and RTK GPS.
  • Environments: ACRE cornfields under various weather conditions and growth stages. P-AgBot drove under the canopies in the cornfields.
  • Challenges: Collected data in environments which contain hanging leaves and rough terrain.

Robot Platform and Sensors

  • Unmanned Ground Vehicle (UGV): Clearpath Jackal (base_link)
  • 3D LiDAR: Two Velodyne VLP-16 units mounted for horizontal (velodyne1) and vertical (velodyne2) scanning.
  • IMU: Internal IMU in UGV (imu_link)
  • Wheel Encoder: Internal wheel encoder in UGV
  • RTK GPS: Emlid M2 (gps_link)

Accessing the Dataset

  • Download the dataset here.

Data Description

Data is sorted by collection date and GPS availability, containing Rosbag (*.bag) files. Each file includes a comprehensive set of sensor measurements.

Figures

  • Coordinate Frames of P-AgBot

Markdown logo

  • TF Tree Visualization

Markdown logo

For details on sensor transformations, see static_transform.txt

Data Folders

Folder Number of Files Size (GB)
with_GPS 18 86.3
without_GPS 14 51.9

ROS Topics

Topic Description ROS Message Type
/cmd_vel Robot linear/angular velocity geometry_msgs/Twist
/gps/fix RTK GPS measurements sensor_msgs/NavSatFix
/imu/data Robot IMU data sensor_msgs/Imu
/ns1/velodyne_points Point cloud from horizontal LiDAR velodyne1 sensor_msgs/PointCloud2
/ns2/velodyne_points Point cloud from vertical LiDAR velodyne2 sensor_msgs/PointCloud2
/odometry/filtered Filtered odometry from wheel encoders and IMU fusion nav_msgs/Odometry
/tf Sensor coordinate frames relationship tf2_msgs/TFMessage

Citations

If you use this dataset for your research, please consider citing our works:

@ARTICLE{10494876,
  author={Kim, Kitae and Deb, Aarya and Cappelleri, David J.},
  journal={IEEE Robotics and Automation Letters}, 
  title={P-AgSLAM: In-Row and Under-Canopy SLAM for Agricultural Monitoring in Cornfields}, 
  year={2024},
  volume={9},
  number={6},
  pages={4982-4989},
  keywords={Feature extraction;Laser radar;Robots;Simultaneous localization and mapping;Three-dimensional displays;Point cloud compression;Monitoring;Agricultural automation;robotics and automation in agriculture and forestry;SLAM},
  doi={10.1109/LRA.2024.3386466}}

@INPROCEEDINGS{10341516,
  author={Deb, Aarya and Kim, Kitae and Cappelleri, David J.},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Deep Learning-Based Leaf Detection for Robotic Physical Sampling with P-AgBot}, 
  year={2023},
  volume={},
  number={},
  pages={8291-8297},
  keywords={YOLO;Deep learning;Three-dimensional displays;Robot kinematics;Robot vision systems;Crops;Grasping},
  doi={10.1109/IROS55552.2023.10341516}}

@ARTICLE{9810180,
  author={Kim, Kitae and Deb, Aarya and Cappelleri, David J.},
  journal={IEEE Robotics and Automation Letters}, 
  title={P-AgBot: In-Row & Under-Canopy Agricultural Robot for Monitoring and Physical Sampling}, 
  year={2022},
  volume={7},
  number={3},
  pages={7942-7949},
  keywords={Crops;Robots;Laser radar;Navigation;Monitoring;Three-dimensional displays;Autonomous robots;Field robots;agricultural automation},
  doi={10.1109/LRA.2022.3187275}}