Attention

As of June 30, 2025, the Isaac ROS Buildfarm for Isaac ROS 2.1 on Ubuntu 20.04 Focal is no longer supported.

Due to an isolated infrastructure event, all ROS 2 Humble Debian packages that were previously built for Ubuntu 20.04 are no longer available in the Isaac Apt Repository. All artifacts for Isaac ROS 3.0 and later are built and maintained with a more robust pipeline.

Users are encouraged to migrate to the latest version of Isaac ROS. The source code for Isaac ROS 2.1 continues to be available on the release-2.1 branches of the Isaac ROS GitHub repositories.

The original documentation for Isaac ROS 2.1 is preserved below.

Tutorial to Run NITROS-Accelerated Graph with Argus Camera

graph LR; argus_node("ArgusMonoNode (Raw Image)") --> rectify_node("RectifyNode (Rectified Image)"); rectify_node --> encoder_node("DnnImageEncoderNode (DNN Pre-Processed Tensors)"); encoder_node --> triton_node("TritonNode (DNN Inference)"); triton_node --> unet_decoder_node("UNetDecoderNode (Segmentation Image)");

If you have an Argus-compatible camera, you can also use the launch file provided in this package to start a fully NITROS-accelerated image segmentation graph.

To start the graph:

  1. Follow the quickstart up to step 7.

  2. Inside the container, install the isaac_ros_argus_camera package.

    sudo apt-get install -y ros-humble-isaac-ros-argus-camera

  3. Run the following launch files to start the graph:

    ros2 launch isaac_ros_unet isaac_ros_argus_unet_triton.launch.py model_name:=peoplesemsegnet_shuffleseg model_repository_paths:=['/tmp/models'] input_binding_names:=['input_2:0'] output_binding_names:=['argmax_1'] network_output_type:='argmax'
  4. In another terminal, visualize and validate the output of the package by launching

    rqt_image_view:

    cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
  ./scripts/run_dev.sh

    Then launch rqt_image_view:

    ros2 run rqt_image_view rqt_image_view

To view a colorized segmentation mask, inside the rqt_image_view GUI, change the topic to /unet/colored_segmentation_mask.

You can also view the raw segmentation, which is published to

/unet/raw_segmentation_mask, where the raw pixels correspond to the class labels making it unsuitable for human visual inspection.