ROS wrapper for ArUco Opencv module

Quick start

1. Prepare the camera topics.

The ROS driver for the camera you are using should publish 2 topics containing: Camera images (most commonlyimage_raw, image_color, image_mono or image_rect topic) and Camera calibration data (camera_info topic). Both topics should be published in the same namespace. Let's assume the driver publishes on these topics:

/camera1/image_raw
/camera1/camera_info

:warning: As of now the wrapper does not support multiple camera sources published on the same topics.

The camera should be calibrated (the better the calibration the better the marker pose estimation). If the camera is uncalibrated (the matrices D, K, R, P in camera_info messages are set to 0) or the calibration is invalid, you should recalibrate the camera.

Also, make sure that the frame_id published with the Camera image headers is not empty.

2. Prepare the marker.

You can use scripts in aruco_opencv package to generate PDF files with ArUco markers.

Let's create a 0.2 x 0.2 m marker (including white margin) with ID 0:

ros2 run aruco_opencv create_marker 0

The output will look 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 will be passed to the marker tracker as a parameter.

Make sure to print the marker in the original scale.

3. Run the marker tracker.

Start the aruco_tracker node with cam_base_topic parameter set to the Camera image topic name and marker_size to the value noted in the previous step:

ros2 run aruco_opencv aruco_tracker --ros-args -p cam_base_topic:=camera1/image_raw -p marker_size:=0.15

If the images are rectified (undistorted), you should also set image_is_rectified parameter to true:

ros2 run aruco_opencv aruco_tracker --ros-args -p cam_base_topic:=camera1/image_rect -p image_is_rectified:=true -p marker_size:=0.075 

aruco_tracker is a Managed (Lifecycle) Node. It will start in unconfigured state, so you will need to configure and activate it manually:

ros2 lifecycle set /aruco_opencv configure
ros2 lifecycle set /aruco_opencv activate

Alternatively you can use aruco_tracker_autostart node which will automatically configure and activate itself:

ros2 run aruco_opencv aruco_tracker_autostart --ros-args -p cam_base_topic:=camera1/image_raw -p marker_size:=0.15

4. Visualize the data.

For each received image, aruco_tracker will publish a message on aruco_detections topic:

ros2 topic echo /aruco_detections

Put the marker in front of the camera. If the marker is detected, the markers array should contain the marker poses.

The marker poses are also published on TF. You can visualize the data in RViz by setting fixed frame to the frame_id of the camera and adding the TF display.

On the /aruco_tracker/debug topic you can see the Camera images with the frame axes of detected markers drawn on top of it.