ArUco Workspace ROI and Object Localization

This ROS2 package uses ArUco markers to define a region of interest (ROI) in a workspace and compute the real-world coordinates of objects within that region. It detects markers, estimates their poses, and calculates the object's actual position based on camera calibration data.

Dependencies

• ROS 2 (Foxy, Galactic, Humble or later)
• OpenCV
• cv_bridge
• sensor_msgs

Installation

1. Clone the repository

Clone this repository into your ROS2 workspace:

cd ~/ros2_ws/src
git clone https://github.com/Leeseunghun03/ros2-aruco-object-estimator.git aruco_workspace

2. Build

cd ~/ros2_ws && colcon build --symlink-install

3. Camera (RGBD)

ros2 launch realsense2_camera rs_align_depth_launch.py

4. Start Estimate

ros2 launch object_estimator object_estimator.py

Example Use Case: Autonomous Feeding Robot

This package can be integrated into an autonomous feeding robot, where ArUco markers are used to estimate tray serving locations. The robot detects the markers placed on a tray and accurately calculates the serving positions, helping the robot to autonomously serve food to individuals in a predefined area. This capability can be used in healthcare, hospitality, and other industries that require automated feeding solutions.

Video