Cube Pose Tracking using OpenCV and ArUco in ROS: A Step-by-Step Guide

https://www.youtube.com/watch?v=cbPF6tuJRhk

Introduction

Welcome to our latest project that combines the power of ArUco marker detection with the versatility of ROS (Robot Operating System). In this article, we’ll guide you through the process of setting up and running our ArUco Marker Detection and Pose Estimation ROS Node. This project is designed to seamlessly integrate with your ROS environment and enhance your robotics and computer vision capabilities.

Project Overview

What is ArUco Marker Detection?

ArUco markers are a type of augmented reality markers that are widely used for camera calibration and pose estimation. They are characterized by their unique patterns that can be easily detected and recognized by computer vision algorithms.

Features of Our ROS Node

- Real-time ArUco marker detection using OpenCV.
- Pose estimation for a cube with ArUco markers.
- Publishing pose information as a `PoseStamped` message.
- Visualizing the detected cube in RViz with the help of RViz markers.

Getting Started

Prerequisites

Before diving into the project, ensure you have the following:

- ROS installed
- OpenCV with ArUco module
- `tf` library
- Python dependencies (`numpy`, `cv2`, `rospy`)

Setup Instructions

1. Clone the GitHub repository:

https://github.com/ammar-n-abbas/aruco-cube-pose-tracking-ros.git

2. Build the ROS workspace:

cd path/to/your/ros/workspace
catkin_make

3. Run the ROS node:

rosrun your_package_name aruco_cube_pose.py

Video Demonstration

Watch our project in action! Click on the image below to view the YouTube video:

https://youtu.be/cbPF6tuJRhk

GitHub Repository

Explore the open-source code and documentation on our GitHub repository:

https://github.com/ammar-n-abbas/aruco-cube-pose-tracking-ros/

Conclusion

We hope this project adds value to your robotics and computer vision endeavors. Feel free to explore the code, experiment with different configurations, and contribute to the development. If you have any questions or feedback, leave a comment on the YouTube video or create an issue on GitHub.

Happy coding!