isaac_ros_ess

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_ess"
   NGC_RESOURCE="isaac_ros_ess_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_ess

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-ess && \
      sudo apt-get install -y ros-humble-isaac-ros-ess-models-install
   

3. Download and install the pre-trained ESS model files:

   sudo apt-get update
   

   ros2 run isaac_ros_ess_models_install install_ess_models.sh --eula
   

Note

Limitations on x86_64: ESS plugins only run with GPU with sm80 and above. This limits the GPU devices on x86_64 to devices with compute_80 and above. For CUDA compute capability details, please refer to cuda-gpus.

Run Launch File

RosbagRealSense CameraHawk CameraZED Camera

1. Continuing inside the Docker container, install the following dependencies:

   sudo apt-get update
   

   sudo apt-get install -y ros-humble-isaac-ros-examples
   

2. Run the following launch file to spin up a demo using quickstart rosbag:

   To run ESS at a threshold of 0.0 (fully dense output):

   ros2 launch isaac_ros_examples isaac_ros_examples.launch.py launch_fragments:=ess_disparity \
      engine_file_path:=${ISAAC_ROS_WS:?}/isaac_ros_assets/models/dnn_stereo_disparity/dnn_stereo_disparity_v4.1.0_onnx/ess.engine \
      threshold:=0.0
   

3. Open a second terminal and attach to the container:

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

4. In the second terminal, play the ESS sample rosbag downloaded in the quickstart assets:

ros2 bag play -l ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_ess/rosbags/ess_rosbag \
   --remap /left/camera_info:=/left/camera_info_rect /right/camera_info:=/right/camera_info_rect

Visualize Output

Visualizer scriptFoxglove

1. Open a terminal and attach to the container:

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

2. In the terminal, visualize and validate the disparity output using the visualizer script:

ros2 run isaac_ros_ess isaac_ros_ess_visualizer.py

With threshold set to 0.4, the example result is:

With threshold set to 0.0, the example result is:

Try More Examples

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

• Tutorial with Isaac Sim
• Tutorial with HAWK with Wide FoV
• Visualize ESS Disparity Output

Troubleshooting

Package not found while launching the visualizer script

Symptom

$ ros2 run isaac_ros_ess isaac_ros_ess_visualizer.py
Package 'isaac_ros_ess' not found

Solution

Use the colcon build --packages-up-to isaac_ros_ess command to build isaac_ros_ess; do not use the --symlink-install option. Run source install/setup.bash after the build.

Problem reserving CacheChange in reader

Symptom

When using a rosbag as input, isaac_ros_ess throws an error if the input topics are published too fast:

[component_container-1] 2022-06-24 09:04:43.584 [RTPS_MSG_IN Error] (ID:281473268431152) Problem reserving CacheChange in reader: 01.0f.cd.10.ab.f2.65.b6.01.00.00.00|0.0.20.4 -> Function processDataMsg

Solution

Make sure that the rosbag has a reasonable size and publish rate.

Isaac ROS Troubleshooting

For solutions to problems with Isaac ROS, please check here.

API

Overview

The isaac_ros_ess package offers functionality to generate a stereo disparity map from stereo images using a trained ESS model. Given a pair of stereo input images, the package generates a continuous disparity image for the left input image.

Usage

ros2 launch isaac_ros_ess isaac_ros_ess.launch.py engine_file_path:=<your ESS engine plan absolute path>

ESSDisparityNode

ROS Parameters

ROS Parameter	Type	Default	Description
engine_file_path	string	N/A - Required	The absolute path to the ESS engine file.
threshold	double	0.40	Threshold value ranges between 0.0 and 1.0 for filtering disparity with confidence. Pixels with confidence less than threshold will be marked as invalid in the disparity output. Value 0.0 means a fully dense disparity output.
image_type	string	"RGB_U8".	The input image encoding type. Supports "RGB_U8" and "BGR_U8". Note that if this parameter is not specified and there is an upstream Isaac ROS NITROS node, the type will be decided automatically.

ROS Topics Subscribed

ROS Topic	Interface	Description
left/image_rect	sensor_msgs/Image	The left image of a stereo pair.
right/image_rect	sensor_msgs/Image	The right image of a stereo pair.
left/camera_info	sensor_msgs/CameraInfo	The left camera model.
right/camera_info	sensor_msgs/CameraInfo	The right camera model.

Note

The images on input topics (left/image_rect and right/image_rect) should be a color image either in rgb8 or bgr8 format.

ROS Topics Published

ROS Topic	Interface	Description
disparity	stereo_msgs/DisparityImage	The continuous stereo disparity estimation.

Input Restrictions

1. The input left and right images must have the same dimension and resolution, and the resolution must be no larger than 1920x1200.

2. Each input pair (left/image_rect, right/image_rect, left/camera_info and right/camera_info) should have the same timestamp; otherwise, the synchronizing module inside the ESS Disparity Node will drop the input with smaller timestamps.

Output Interpretations

1. The isaac_ros_ess package outputs a disparity image with dimension same as the ESS model output dimension.

   ESS Model	Output Dimension
   ess.onnx	960 x 576
   light_ess.onnx	480 x 288

   The input images are rescaled to the ESS model input dimension before inferencing. There are two outputs from the ESS model with the same dimension: disparity output and confidence output. The disparity is filtered with confidence using a pre-configured threshold. Pixels with confidence less than the threshold is replaced with -1.0 as invalid before the inference result is published. For fully dense disparity output without confidence thresholding, set the threshold to 0.0.

2. The left and right CameraInfo are used to composite a stereo_msgs/DisparityImage. If you only care about the disparity image, and don’t need the baseline and focal length information, you can pass dummy camera messages.