fast_idler_supports_detection

Fast detection of idler supports in various environments using ROS 2 with PCL and density histograms based on PointCloud2 data.

Publications

If you use this work in an academic context, please cite the following publication:

J. Jakubiak, J. Delicat, "Fast detection of idler supports using density histograms in belt conveyors inspection with a mobile robot"

@Article{app142310774,
    author = {Jakubiak, Janusz and Delicat, Jakub},
    title = {Fast Detection of Idler Supports Using Density Histograms in Belt Conveyor Inspection with a Mobile Robot},
    journal = {Applied Sciences},
    volume = {14},
    year = {2024},
    number = {23},
    article-number = {10774},
    url = {https://www.mdpi.com/2076-3417/14/23/10774},
    issn = {2076-3417},
    doi = {10.3390/app142310774}
}

Dataset

You can download the dataset used in our experiments from the following link:

Download Dataset

Installation

Clone the repository and its submodules:

mkdir -p fast_idler_supports_detection/src
cd fast_idler_supports_detection
git clone [email protected]:jdelicat/idlers_detection.git --recursive src

Install dependencies using rosdep:

rosdep install --from-paths src --ignore-src -r -y
colcon  build --symlink-install
source install/setup.bash

Running the Algorithm

Run the detection algorithm and save the output to a YAML file:

ros2 launch fast_idler_supports_detection fast_idler_supports_detection.launch.py bag_file_path:=$(pwd)/path_a_pointcloud_public