All you need to know about how to install ROS2 on Jetson Orin – using NileCAM81

As you’re already aware, NVIDIA’s Jetson AGX Orin™ is rapidly emerging as a perfect ideal platform for building autonomous robotic systems – powered by edge AI. After all, this platform is unmatched in performance and efficiency as it gives you the flexibility to create the next generation of AI solutions with the latest NVIDIA GPU technology.

NVIDIA® Jetson Orin™ modules have up to 275 Trillion Operations per Second (TOPS) and 8X the performance of the last generation for multiple concurrent AI inference pipelines. Furthermore, they come with high-speed interface support for multiple sensors – making them the ideal solution for a new age of robotics.

In this blog, you’ll discover how to get started working with ROS2 Foxy on Nvidia Jetson AGX Orin for robotics applications.

You’ll also get to know how to work with an embedded camera in the ROS workspace.

We used the NileCAM81, an 8MP GMSL2 camera developed by e-con Systems, to demonstrate this application. It can stream high frame rate uncompressed video data of HD at 60 fps, FHD at 60 fps, and 8MP at 16 fps up to a distance of 15 meters with very low latency.

Prerequisites of installing ROS2 on Orin

Before we get started on learning to use the ROS2 Foxy on Nvidia Jetson AGX Orin – let’s find out the prerequisites so that you aren’t caught off-guard later.

1. Nvidia AGX Orin flashed with the latest jetpack (Currently, Jetpack 5.0.2 is installed)
2. NileCAM81 – Camera Binary should be installed (Any camera can be used)

Now, let’s understand how to install ROS2 on Orin.

How to install ROS2 on Orin

Please follow the official steps for installing ROS here.

Set up the workspace

1. Open the Terminal and create a workspace and move to the folder

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src/

2. Add the ROS tutorials to the workspace

git clone https://github.com/ros/ros_tutorials.git -b foxy-devel

3. Add the package dependencies

cd ~/ros2_ws/
rosdep install -i --from-path src --rosdistro foxy -y

4. Colcon Build – Build your workspace

sudo apt update
sudo apt install python3-colcon-common-extensions
colcon build

5. Source the created workspace in Bash file

sudo apt-get install gedit
gedit ~/.bashrc

Add the following line to the bottom of bashrc file

source ~/ros2/install/setup.bash

6. Create a Package

Open a new terminal

cd ~/ros2_ws/src

Now, let’s create a package named cv_basics.

ros2 pkg create --build-type ament_python cv_basics --dependencies rclpy image_transport cv_bridge sensor_msgs std_msgs opencv2

7. Create the Python script for Publisher and Subscriber

Move to the cv_basics package

cd ~/dev_ws/src/cv_basics/cv_basics

Create the Pub script

gedit webcam_pub.py

Copy and paste the below code:

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
class ImagePublisher(Node):
def __init__(self):
super().__init__('image_publisher')
self.publisher_ = self.create_publisher(Image, 'video_frames', 10)
timer_period = 0.1
self.timer = self.create_timer(timer_period, self.timer_callback)
self.cap = cv2.VideoCapture(0)
self.br = CvBridge()
def timer_callback(self):
ret, frame = self.cap.read()
if ret == True:
self.publisher_.publish(self.br.cv2_to_imgmsg(frame))
self.get_logger().info('Publishing video frame')
def main(args=None):
rclpy.init(args=args)
image_publisher = ImagePublisher()
rclpy.spin(image_publisher)
image_publisher.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

Create the Sub script

gedit webcam_sub.py

Copy and paste the below code:

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
class ImageSubscriber(Node):
def __init__(self):
super().__init__('image_subscriber')
self.subscription = self.create_subscription(
Image,
'video_frames',
self.listener_callback,
10)
self.subscription
self.br = CvBridge()
def listener_callback(self, data):
self.get_logger().info('Receiving video frame')
current_frame = self.br.imgmsg_to_cv2(data)
cv2.imshow("camera", current_frame)
cv2.waitKey(1)
def main(args=None):
rclpy.init(args=args)
image_subscriber = ImageSubscriber()
rclpy.spin(image_subscriber)
image_subscriber.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

8. Modify Setup.py

Move to the Setup.py

cd ~/ros2_ws/src/cv_basics/
gedit setup.py
entry_points={
'console_scripts': [
'img_publisher = cv_basics.webcam_pub:main',
'img_subscriber = cv_basics.webcam_sub:main',
],
},

9. Build the package

cd ~/dev_ws/
rosdep install -i --from-path src --rosdistro foxy –y

If the above command throws any error, then before building make sure the dependencies are named as below

cd ~/dev_ws/src/cv_basics

gedit package.xml

<depend>rclpy</depend>
<depend>image_transport</depend>
<depend>cv_bridge</depend>
<depend>sensor_msgs</depend>
<depend>std_msgs</depend>
<depend>python-opencv</depend>

Build the Packages:

colcon build --packages-select cv_basics

10. Running the ROS nodes.

Finally, all the setup is done. Now let us check the output.

Open the terminal

cd ~/ros2_ws/
ros2 run cv_basics img_publisher

Open the other terminal

ros2 run cv_basics img_subscriber

As you can see below, a camera window will then pop up.

Final note

We hope we were able to break down the nitty-gritty of how to effortlessly start working with the ROS2 Foxy on NVidia Jetson AGX Orin for robotics applications.

Stay tuned for our next blog, as we will see how to use face detection capabilities by using OpenCV.

For more details about the camera, please visit our product page. To check out e-con Systems’ entire camera portfolio, explore our Camera Selector page.

Please note that this content has been produced by referencing the official documents of ROS2. You can find them here. We also referred to the material published by Automatic Addison.