In this tutorial, I will show you how to publish a coordinate transformation between an ArUco marker and a camera (which is usually mounted on the robot). The ArUco marker coordinate frame will be labeled aruco_marker (child frame), and the camera frame will be labeled camera_depth_frame (parent frame).

Here is the output we will achieve:

We will also see how to visualize the coordinate transformation in RViz.

Robotics companies like Boston Dynamics use ArUco markers to precisely connect and recharge at a docking station.

Table of Contents

Prerequisites

ROS 2 Galactic installed on Ubuntu Linux 20.04
You have already created a ROS 2 workspace. The name of my workspace is dev_ws.
You have completed this tutorial to make sure OpenCV works with ROS 2 on your computer.
You have created an ArUco marker.
You have OpenCV (Python) installed on your system (pip install opencv-contrib-python).
(Optional) You are able to run the code on this tutorial without any problems.

The link to the opencv_tools package that contains all the full code in this tutorial is here on my Google Drive.

Create the Code

Open a new terminal window, and type:

cd ~/dev_ws/src/opencv_tools/opencv_tools

Open a new Python file named aruco_marker_pose_estimation.py.

gedit aruco_marker_pose_estimation_tf.py

Type the code below into it:

# Publishes a coordinate transformation between an ArUco marker and a camera
# Author:
# - Addison Sears-Collins
# - https://automaticaddison.com
  
# Import the necessary ROS 2 libraries
import rclpy # Python library for ROS 2
from rclpy.node import Node # Handles the creation of nodes
from cv_bridge import CvBridge # Package to convert between ROS and OpenCV Images
from geometry_msgs.msg import TransformStamped # Handles TransformStamped message
from sensor_msgs.msg import Image # Image is the message type
from tf2_ros import TransformBroadcaster

# Import Python libraries
import cv2 # OpenCV library
import numpy as np # Import Numpy library
from scipy.spatial.transform import Rotation as R

# The different ArUco dictionaries built into the OpenCV library. 
ARUCO_DICT = {
  "DICT_4X4_50": cv2.aruco.DICT_4X4_50,
  "DICT_4X4_100": cv2.aruco.DICT_4X4_100,
  "DICT_4X4_250": cv2.aruco.DICT_4X4_250,
  "DICT_4X4_1000": cv2.aruco.DICT_4X4_1000,
  "DICT_5X5_50": cv2.aruco.DICT_5X5_50,
  "DICT_5X5_100": cv2.aruco.DICT_5X5_100,
  "DICT_5X5_250": cv2.aruco.DICT_5X5_250,
  "DICT_5X5_1000": cv2.aruco.DICT_5X5_1000,
  "DICT_6X6_50": cv2.aruco.DICT_6X6_50,
  "DICT_6X6_100": cv2.aruco.DICT_6X6_100,
  "DICT_6X6_250": cv2.aruco.DICT_6X6_250,
  "DICT_6X6_1000": cv2.aruco.DICT_6X6_1000,
  "DICT_7X7_50": cv2.aruco.DICT_7X7_50,
  "DICT_7X7_100": cv2.aruco.DICT_7X7_100,
  "DICT_7X7_250": cv2.aruco.DICT_7X7_250,
  "DICT_7X7_1000": cv2.aruco.DICT_7X7_1000,
  "DICT_ARUCO_ORIGINAL": cv2.aruco.DICT_ARUCO_ORIGINAL
}

class ArucoNode(Node):
  """
  Create an ArucoNode class, which is a subclass of the Node class.
  """
  def __init__(self):
    """
    Class constructor to set up the node
    """
    # Initiate the Node class's constructor and give it a name
    super().__init__('aruco_node')

    # Declare parameters
    self.declare_parameter("aruco_dictionary_name", "DICT_ARUCO_ORIGINAL")
    self.declare_parameter("aruco_marker_side_length", 0.0785)
    self.declare_parameter("camera_calibration_parameters_filename", "/home/focalfossa/dev_ws/src/opencv_tools/opencv_tools/calibration_chessboard.yaml")
    self.declare_parameter("image_topic", "/video_frames")
    self.declare_parameter("aruco_marker_name", "aruco_marker")
    
    # Read parameters
    aruco_dictionary_name = self.get_parameter("aruco_dictionary_name").get_parameter_value().string_value
    self.aruco_marker_side_length = self.get_parameter("aruco_marker_side_length").get_parameter_value().double_value
    self.camera_calibration_parameters_filename = self.get_parameter(
      "camera_calibration_parameters_filename").get_parameter_value().string_value
    image_topic = self.get_parameter("image_topic").get_parameter_value().string_value
    self.aruco_marker_name = self.get_parameter("aruco_marker_name").get_parameter_value().string_value

    # Check that we have a valid ArUco marker
    if ARUCO_DICT.get(aruco_dictionary_name, None) is None:
      self.get_logger().info("[INFO] ArUCo tag of '{}' is not supported".format(
        args["type"]))
        
    # Load the camera parameters from the saved file
    cv_file = cv2.FileStorage(
      self.camera_calibration_parameters_filename, cv2.FILE_STORAGE_READ) 
    self.mtx = cv_file.getNode('K').mat()
    self.dst = cv_file.getNode('D').mat()
    cv_file.release()
    
    # Load the ArUco dictionary
    self.get_logger().info("[INFO] detecting '{}' markers...".format(
  	  aruco_dictionary_name))
    self.this_aruco_dictionary = cv2.aruco.Dictionary_get(ARUCO_DICT[aruco_dictionary_name])
    self.this_aruco_parameters = cv2.aruco.DetectorParameters_create()
     
    # Create the subscriber. This subscriber will receive an Image
    # from the video_frames topic. The queue size is 10 messages.
    self.subscription = self.create_subscription(
      Image, 
      image_topic, 
      self.listener_callback, 
      10)
    self.subscription # prevent unused variable warning
      
    # Initialize the transform broadcaster
    self.tfbroadcaster = TransformBroadcaster(self)
      
    # Used to convert between ROS and OpenCV images
    self.bridge = CvBridge()
   
  def listener_callback(self, data):
    """
    Callback function.
    """
    # Display the message on the console
    self.get_logger().info('Receiving video frame')
 
    # Convert ROS Image message to OpenCV image
    current_frame = self.bridge.imgmsg_to_cv2(data)
    
    # Detect ArUco markers in the video frame
    (corners, marker_ids, rejected) = cv2.aruco.detectMarkers(
      current_frame, self.this_aruco_dictionary, parameters=self.this_aruco_parameters,
      cameraMatrix=self.mtx, distCoeff=self.dst)

    # Check that at least one ArUco marker was detected
    if marker_ids is not None:
    
      # Draw a square around detected markers in the video frame
      cv2.aruco.drawDetectedMarkers(current_frame, corners, marker_ids)

      # Get the rotation and translation vectors
      rvecs, tvecs, obj_points = cv2.aruco.estimatePoseSingleMarkers(
        corners,
        self.aruco_marker_side_length,
        self.mtx,
        self.dst)
        
      # The pose of the marker is with respect to the camera lens frame.
      # Imagine you are looking through the camera viewfinder, 
      # the camera lens frame's:
      # x-axis points to the right
      # y-axis points straight down towards your toes
      # z-axis points straight ahead away from your eye, out of the camera
      for i, marker_id in enumerate(marker_ids):  

        # Create the coordinate transform
        t = TransformStamped()
        t.header.stamp = self.get_clock().now().to_msg()
        t.header.frame_id = 'camera_depth_frame'
        t.child_frame_id = self.aruco_marker_name
      
        # Store the translation (i.e. position) information
        t.transform.translation.x = tvecs[i][0][0]
        t.transform.translation.y = tvecs[i][0][1]
        t.transform.translation.z = tvecs[i][0][2]

        # Store the rotation information
        rotation_matrix = np.eye(4)
        rotation_matrix[0:3, 0:3] = cv2.Rodrigues(np.array(rvecs[i][0]))[0]
        r = R.from_matrix(rotation_matrix[0:3, 0:3])
        quat = r.as_quat()   
        
        # Quaternion format     
        t.transform.rotation.x = quat[0] 
        t.transform.rotation.y = quat[1] 
        t.transform.rotation.z = quat[2] 
        t.transform.rotation.w = quat[3] 

        # Send the transform
        self.tfbroadcaster.sendTransform(t)    
                  
        # Draw the axes on the marker
        cv2.aruco.drawAxis(current_frame, self.mtx, self.dst, rvecs[i], tvecs[i], 0.05)        
              
    # Display image
    cv2.imshow("camera", current_frame)
    
    cv2.waitKey(1)
  
def main(args=None):
  
  # Initialize the rclpy library
  rclpy.init(args=args)
  
  # Create the node
  aruco_node = ArucoNode()
  
  # Spin the node so the callback function is called.
  rclpy.spin(aruco_node)
  
  # Destroy the node explicitly
  # (optional - otherwise it will be done automatically
  # when the garbage collector destroys the node object)
  aruco_node.destroy_node()
  
  # Shutdown the ROS client library for Python
  rclpy.shutdown()
  
if __name__ == '__main__':
  main()

Save the file, and close it.

Open the package.xml file by opening a new terminal window, and typing:

cd ~/dev_ws/src/opencv_tools/

gedit package.xml

Add the following lines to the package.xml file in the appropriate location.

<depend>geometry_msgs</depend>
<depend>tf2_ros</depend>
<depend>tf_transformations</depend>

Save the file, and close it.

Now open the setup.py file.

gedit setup.py

Add the following line under the ‘img_subscriber’ = …. line.

'aruco_marker_pose_estimator_tf = opencv_tools.aruco_marker_pose_estimation_tf:main',

Save the file, and close it.

Build the package:

cd ~/dev_ws/

colcon build

Run the Nodes

To run the nodes, open a new terminal window.

Make sure you are in the root of your workspace:

cd ~/dev_ws/

Run the image publisher node. If you recall, its name is img_publisher.

ros2 run opencv_tools img_publisher

Open a new terminal window, and type:

ros2 run opencv_tools img_publisher

Open another terminal window, and type:

ros2 run opencv_tools aruco_marker_pose_estimator_tf

Let’s check out the topics.

ros2 topic list

Let’s see the tf topic.

ros2 topic echo /tf

How to Visualize an ArUco Marker Pose in RViz

Now open a new terminal window, and type:

rviz2

Change the Fixed Frame to camera_depth_frame.

Click the Add button on the bottom-left.

Scroll down to TF and add the TF plugin.

You should see the following output.