How do you activate vision on a robot using ROS?

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Vision is a crucial skill for robots that need to interact with their environment, recognize objects, and perform tasks. However, setting up vision on a robot using ROS can be challenging, especially for beginners. In this article, you will learn how to activate vision on a robot using ROS in four steps: installing the necessary packages, configuring the camera, launching the vision nodes, and testing the results.

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1 Install packages

The first step is to install the packages that provide the basic functionality for vision in ROS. These include image_transport, which handles the compression and transport of images, cv_bridge, which converts between ROS and OpenCV formats, and image_proc, which performs some common image processing tasks. You can install these packages using the command: sudo apt-get install ros-$ROS_DISTRO-image-transport ros-$ROS_DISTRO-cv-bridge ros-$ROS_DISTRO-image-proc where $ROS_DISTRO is the name of your ROS distribution, such as noetic or melodic.

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Hayk Hakobyan

Senior Python Developer
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in my experience I have use cv-bridge. Better and easier to use cv-bridge in noetic version, because noetic python version is 3 by default, and with OpenCV easy to work with python3. for easy install can use <sudo apt-get install ros-$ROS_DISTRO-image-transport ros-$ROS_DISTRO-cv-bridge>

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2 Configure camera

The second step is to configure your camera to work with ROS. You need to make sure that your camera is compatible with the Video4Linux (V4L) interface, which is the standard way of accessing video devices on Linux. You can check this by running the command: v4l2-ctl --list-devices which should show your camera name and device number. You also need to create a launch file that sets the camera parameters, such as resolution, frame rate, and calibration. You can use the usb_cam package as an example, which provides a node that publishes raw and compressed images from a USB camera. You can install this package using the command: sudo apt-get install ros-$ROS_DISTRO-usb-cam and then copy and modify the launch file from the package's launch folder.

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Steeve Daniel

Machine Learning Developer Analyst @EnQubes| Machine Learning | Computer Vision | Deep Learning | Conversational LLMs | M.S. in Robotics & Autonomous Systems Engineering
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While the Video4Linux (V4L) interface is a widely used standard for accessing video devices on Linux, it's worth noting that not all cameras may have native V4L support. Some cameras might require additional drivers or modifications to work seamlessly with ROS. Therefore, before choosing a camera for your robot, it's essential to research its compatibility and community support within the ROS ecosystem. Additionally, if you encounter any issues with your camera configuration, consider reaching out to ROS forums or relevant communities to seek guidance and solutions from experienced users and developers.

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Hayk Hakobyan

Senior Python Developer
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in my practice I didn't use ros-$ROS_DISTRO-usb-cam module i done task like this 1. create node 2. read image from camera with OpenCV 3. transfer raw information with camera topic 4. check in Rviz camera recognize and shows image

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Tim Adamson

Tech Leader in AI, Robotics, and Blockchain | Leading Engineering Teams to Deliver
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I would probably just make sure to buy a camera that has open source ROS drivers available. This can simplify the process significantly because it will allow you to just run the drivers and get your image stream in ROS immediately without having to add extra nodes and configurations.

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3 Launch vision nodes

The third step is to launch the vision nodes that will process and display the images from your camera. You can use the image_view package, which provides a node that subscribes to an image topic and shows it in a window. You can install this package using the command: sudo apt-get install ros-$ROS_DISTRO-image-view and then create a launch file that runs the image_view node and sets the image topic to match your camera topic. You can also run other vision nodes that perform tasks such as edge detection, feature extraction, or object recognition, depending on your needs and goals. You can find a list of available vision packages on the ROS wiki.

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Tim Adamson

Tech Leader in AI, Robotics, and Blockchain | Leading Engineering Teams to Deliver
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I like using image_view to make sure that the camera is working and to see the perspective from different cameras if the robot has a few. It can be a little bit buggy sometimes though and when you change the topic you are subscribing to, you sometimes need to refresh it. It can also be helpful to use image_view to visualize a depth image as well.

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Hayk Hakobyan

Senior Python Developer
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in my experience I never checked modules like that, I write that module by myself. after reading image information from ros topic I every time converting it to OpenCV format and use edge detection or object recognition on it

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4 Test results

The final step is to test the results of your vision setup and make sure that everything works as expected. You can use the rostopic and rosrun commands to check the topics and nodes that are active, and the rqt_image_view tool to visualize the images from different topics. You can also use the rosbag command to record and play back the images for further analysis or debugging. You can find more information on how to use these tools on the ROS wiki.

By following these four steps, you should be able to activate vision on a robot using ROS and explore the possibilities of ROS perception and vision. You can also customize your vision setup by adding or changing packages, parameters, or algorithms, depending on your robot and application.

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