ArUco Marker tracking

An ArUco tag is a type of fiducial marker widely used in robotics and computer vision to support tasks such as localization, mapping, and navigation. These visual markers provide a reliable, easily detectable point of reference that helps robots perceive and interpret their environment with precision.

In this example, we will use ros_aruco_opencv package for detecting individual markers.

As sending raw images from the camera via wireless network may be insufficient, we will relay all the processing to the Raspberry Pi.

What to expect?

After this tutorial is completed, you should end up with something like this:

The detected ArUco Tags are also published to /aruco_detections topic, so you could use the output in your custom nodes.

Prerequisites

📄Connect via SSH

Learn how to establish an SSH connection with your Leo Rover and access its terminal using Putty or OpenSSH.

📄Connect to a local network and the Internet

Learn how to connect your Leo Rover to a local network and the internet to download files and forward internet to your computer.

Software integration

Installing the packages

Start by logging in into your rover via SSH:

📄Connect via SSH

Learn how to establish an SSH connection with your Leo Rover and access its terminal using Putty or OpenSSH.

Install aruco-opencv package:

sudo apt install ros-${ROS_DISTRO}-aruco-opencv

Creating launch and configuration files

To launch the tracker, you can use the launch file from the installed aruco-opencv package or you can create your own. In this guide, we will show you how to create a launch file which provides basic config for detecting single markers.

In /etc/ros, create a yaml configuration file. This simple example sets the basic parameters for the node.

/etc/ros/tracker.yaml

/**:
  ros__parameters:
    cam_base_topic: camera/image_color
    output_frame: 'base_link'

    marker_dict: 4X4_50

    publish_tf: true
    marker_size: 0.15

    aruco:
      adaptiveThreshWinSizeMin: 3
      adaptiveThreshWinSizeMax: 23
      adaptiveThreshWinSizeStep: 10

info

You will most likely need to change marker_size parameter depending on the actual size of your printed ArUco tag.

The parameters in aruco namespace are dynamic reconfigure parameters - which means you can change them as the node is working using rqt (in dynamic reconfigure plugin). You can play with their values and then add chosen parameters to the yaml file.

You can find a detailed description of each parameter in the aruco namespace here.

Next, also in /etc/ros, create a launch file that will use the previously added configuration with the tracker node.

/etc/ros/tracker.launch.xml

<launch version="0.1.1">
  <node pkg="aruco_opencv" exec="aruco_tracker_autostart">
    <param from="/etc/ros/tracker.yaml" />
  </node>
</launch>

Now you can run the tracker with this command:

ros2 launch /etc/ros/tracker.launch.xml

or you can include your launch file in the robot.launch.xml file so that the node will start at boot. To do so, put this line somewhere before the closing </launch> tag.

/etc/ros/robot.launch.xml

<include file="/etc/ros/tracker.launch.xml"/>

Now you need to reboot the rover, or restart the ros nodes

ros-nodes-restart

Generate ArUco markers

Now, we need to create some markers, so go back to your computer.

If you don't have ROS installed, you can follow this guide:

📄Install ROS on your computer

Learn how to install the Robot Operating System (ROS) on your computer. Step-by-step guide for beginners.

Install the aruco_opencv package:

sudo apt install ros-${ROS_DISTRO}-aruco-opencv

And run the create_marker script:

ros2 run aruco_opencv create_marker 0

tip

You can also run

ros2 run aruco_opencv create_marker --help

to see the help message with desctription of all available options.

This will create $0.2$x$0.2$m marker with id 0 and save it to markers.pdf file. The script will also print some information looking like this

ArUco dictionary: 4X4_50
Inner marker bits: 4
Marker border bits: 1
Pixels per bit: 1
Margin pixels: 1
Marker side size: 0.1500 m - 6 pixels
Output image side size: 0.200 m - 8 pixels
Output DPI: 1.016
Generating marker with ID 0...
Converting images to pdf and writing to output file markers.pdf...

Please note the Marker side size value. It is the value that should be provided to marker_size parameter in the config yaml file.

Make sure to print the marker in the original scale.

warning

Due to differences in printer setups, the actual size of the printed marker may be different. Make sure the marker_size parameter represents the actual size (in meters) of the ArUco tag.

Visualization

The detected ArUco tags are published on aruco_detections topic. You can see their positions with

ros2 topic echo /aruco_detections

You can get a more graphical representation of the data with RViz as with our config, the marker poses are also publihsed as TF.

Open RViz by typing rviz2 in the terminal and set Fixed Frame to base_link.

Now on the Displays panel click Add -> By display type and search for TF and click Ok.

As there are many TF frames for the rover itself, to make it easier to distinguish check the Show Names option and from the Frames dropdown uncheck All Enabled - we only want base_link and any frame named like marker_x where x is the id of the detected marker. Find those frames and check their boxes to make them appear in RViz.

tip

If you can't find any marker_x frame it's due to no marker being detected. Make sure that the robot's camera can see your marker, and detects it.

On /aruco_tracker/debug topic you can see the Camera images with the frame axes of detected markers drawn on top of it. To see those images you need to click on the Displays panel Add -> By topic and search for aruco_tracker. There you will have debug topic for which you will be able to choose Image display. Click it and confirm with Ok.

Your final setup should look more or less like this:

What next?

With this tutorial completed you may be wondering what to do next. Our pick would be the ARTag follower. However, you can check out other examples from leo_examples repository (like line follower and object detection). You can also check out other integrations from our Integrations site.