Visual Multi Crop Row Navigation in Arable Farming Fields (ROS2)

A work presented in IROS 2022 - Kyoto, Japan

Check out the video1, of our robot following this approach to navigate on a real multi lace row-crop field (beans field).

pyCUDA installation (untested on this version)

May have success adapting the below for https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64$
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_install$
sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda

Dependencies

Ubuntu 22.04 & ROS2 Humble
sudo apt-get install python3-shapely python3-opencv python3-pip
pip3 install future scipy

Install tf2_geometry_msgs from source (because we need the do_transform_pose which is going to be merged in this PR)

cd my_ws/src/
git clone https://github.com/ros2/geometry2.git
cd geometry2
git fetch origin pull/533/head:pr-533
git checkout pr-533

ROS dependencies

rosdep install --from-paths src --ignore-src -r -y

Build and run

navigate to your colcon workspace folder, e.g.:

cd my_ws/

clean:

rm -rf build/ install/ log
colcon build

source setup file:

source ./install/setup.bash

launch main script:

ros2 launch visual_multi_crop_row_navigation vs_navigation.launch.xml

Multi-Crop Row Navigation Datasets

used for analyze the robustness of crop-row detection technique as a quantitative evaluate. For each of the five crops (sugar_beet, potato, beans, lemon-balm and coriander) and three simulated fields, 100 images were annotated using data from BonnBot-I where the camera tilt angle ρ was varied from 55◦ to 75◦. The annotations contain ground-truth of all the lines located underneath the robot. To measure the accuracy we compare the predicted lines of each image to the ground-truth using two parameters: position and orientation. The position of a line is defined based on its intersection with bottom edge of the image, where the distance between the prediction and the ground truth is normalized based on the width of the image.

for accessing dataset please sned an email to: Alireza.ahmadi@uni-bonn.de

Alternatively this may be useful: https://github.com/Hamza-MAILKA/Crop_Row_Dataset

Citation

if you use this project in your recent works please refernce to it by:

@article{ahmadi2021towards,
  title={Towards Autonomous Crop-Agnostic Visual Navigation in Arable Fields},
  author={Ahmadi, Alireza and Halstead, Michael and McCool, Chris},
  journal={arXiv preprint arXiv:2109.11936},
  year={2021}
}

@inproceedings{ahmadi2020visual,
  title={Visual servoing-based navigation for monitoring row-crop fields},
  author={Ahmadi, Alireza and Nardi, Lorenzo and Chebrolu, Nived and Stachniss, Cyrill},
  booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={4920--4926},
  year={2020},
  organization={IEEE}
}

Acknowledgments

This work has been supported by the German Research Foundation under Germany’s Excellence Strategy, EXC-2070 - 390732324 (PhenoRob) and Bonn AgRobotics Group