isaac_ros_stereo_image_proc

Source code on GitHub.

Quickstart

Set Up Development Environment

1. Set up your development environment by following the instructions in getting started.

2. Clone isaac_ros_common under ${ISAAC_ROS_WS}/src.

   cd ${ISAAC_ROS_WS}/src && \
      git clone -b release-3.2 https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common.git isaac_ros_common
   

3. (Optional) Install dependencies for any sensors you want to use by following the sensor-specific guides.

   Note

   We strongly recommend installing all sensor dependencies before starting any quickstarts. Some sensor dependencies require restarting the Isaac ROS Dev container during installation, which will interrupt the quickstart process.

Download Quickstart Assets

1. Download quickstart data from NGC:

   Make sure required libraries are installed.

   sudo apt-get install -y curl jq tar
   

   Then, run these commands to download the asset from NGC:

   NGC_ORG="nvidia"
   NGC_TEAM="isaac"
   PACKAGE_NAME="isaac_ros_stereo_image_proc"
   NGC_RESOURCE="isaac_ros_stereo_image_proc_assets"
   NGC_FILENAME="quickstart.tar.gz"
   MAJOR_VERSION=3
   MINOR_VERSION=2
   VERSION_REQ_URL="https://catalog.ngc.nvidia.com/api/resources/versions?orgName=$NGC_ORG&teamName=$NGC_TEAM&name=$NGC_RESOURCE&isPublic=true&pageNumber=0&pageSize=100&sortOrder=CREATED_DATE_DESC"
   AVAILABLE_VERSIONS=$(curl -s \
       -H "Accept: application/json" "$VERSION_REQ_URL")
   LATEST_VERSION_ID=$(echo $AVAILABLE_VERSIONS | jq -r "
       .recipeVersions[]
       | .versionId as \$v
       | \$v | select(test(\"^\\\\d+\\\\.\\\\d+\\\\.\\\\d+$\"))
       | split(\".\") | {major: .[0]|tonumber, minor: .[1]|tonumber, patch: .[2]|tonumber}
       | select(.major == $MAJOR_VERSION and .minor <= $MINOR_VERSION)
       | \$v
       " | sort -V | tail -n 1
   )
   if [ -z "$LATEST_VERSION_ID" ]; then
       echo "No corresponding version found for Isaac ROS $MAJOR_VERSION.$MINOR_VERSION"
       echo "Found versions:"
       echo $AVAILABLE_VERSIONS | jq -r '.recipeVersions[].versionId'
   else
       mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets && \
       FILE_REQ_URL="https://api.ngc.nvidia.com/v2/resources/$NGC_ORG/$NGC_TEAM/$NGC_RESOURCE/\
   versions/$LATEST_VERSION_ID/files/$NGC_FILENAME" && \
       curl -LO --request GET "${FILE_REQ_URL}" && \
       tar -xf ${NGC_FILENAME} -C ${ISAAC_ROS_WS}/isaac_ros_assets && \
       rm ${NGC_FILENAME}
   fi
   

Build isaac_ros_stereo_image_proc

Binary PackageBuild from Source

1. Launch the Docker container using the run_dev.sh script:

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

2. Install the prebuilt Debian package:

   sudo apt-get update
   

   sudo apt-get install -y  ros-humble-isaac-ros-image-proc ros-humble-isaac-ros-stereo-image-proc
   

Run Launch File

RealSense CameraHawk CameraZED Camera

1. Ensure that you have already set up your RealSense camera using the RealSense setup tutorial. If you have not, please set up the sensor and then restart this quickstart from the beginning.

2. Continuing inside the container, install the following dependencies:

   sudo apt-get update
   

   sudo apt-get install -y ros-humble-isaac-ros-examples ros-humble-isaac-ros-realsense ros-humble-isaac-ros-depth-image-proc ros-humble-isaac-ros-image-proc
   

3. Run the launch file, which launches the example, and wait for 10 seconds.

   ros2 launch isaac_ros_examples isaac_ros_examples.launch.py launch_fragments:=realsense_stereo_rect,disparity,disparity_to_depth,point_cloud_xyz
   

4. Observe point cloud output /points on a separate terminal with the command:

   ros2 topic echo /points
   

Note

For RealSense camera package issues, please refer to the section here.

Other supported cameras can be found here.

Try More Examples

To continue your exploration, check out the following suggested examples:

• Tutorial with Isaac Sim

API

Overview

The isaac_ros_stereo_image_proc package offers functionality for handling image pairs from a binocular/stereo camera setup, calculating the disparity between the two images, and producing a point cloud with depth information. It largely replaces the stereo_image_proc package.

Available Components

Component	Topics Subscribed	Topics Published	Parameters
DisparityNode	left/image_rect, left/camera_info: The left camera stream right/image_rect, right/camera_info: The right camera stream	disparity: The disparity between the two cameras	max_disparity: The maximum value for disparity per pixel, which is 64 by default. With ORIN backend, this value must be 128 or 256. backends: The VPI backend to use, which is CUDA by default (options: “CUDA”, “XAVIER”, “ORIN”) confidence_threshold: The confidence threshold for VPI SGM algorithm window_size: The window size for SGM disparity calculation num_passes: The number of passes SGM takes to compute result p1: Penalty on disparity changes of +/- 1 between neighbor pixels p2: Penalty on disparity changes of more than 1 between neighbor pixels p2_alpha: Alpha for P2 quality: Quality of disparity output. It’s only applicable when using XAVIER backend. The higher the value, better the quality and possibly slower performance. Refer tothis VPI docfor more details on the parameters
PointCloudNode	left/image_rect_color: The coloring for the point cloud left/camera_info: The left camera info right/camera_info: The right camera info disparity The disparity between the two cameras	points: The output point cloud	use_color: Whether or not the output point cloud should have color. The default value is false. unit_scaling: The amount to scale the xyz points by
DisparityToDepthNode	disparity The disparity image	depth The resultant depth image	N/A

Note

DisparityNode with the ORIN backend requires a max_disparity value of 128 or 256, but the default value is 64. Besides, the ORIN backend requires nv12 input image format, you can use the ImageFormatConverterNode to convert the input to nv12 format.