isaac_ros_rtdetr

Source code available on GitHub.

Stable object detections using SyntheticaDETR in a difficult scene with camera motion blur, round objects with few features, reflective object material, and light reflections

Quickstart

Set Up Development Environment

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

2. (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 development environment 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_rtdetr"
   NGC_RESOURCE="isaac_ros_rtdetr_assets"
   NGC_FILENAME="quickstart.tar.gz"
   MAJOR_VERSION=4
   MINOR_VERSION=0
   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_rtdetr

Binary PackageBuild from Source

1. Activate the Isaac ROS environment:

   isaac-ros activate
   

2. Install the prebuilt Debian package:

   sudo apt-get update
   

   sudo apt-get install -y ros-jazzy-isaac-ros-rtdetr && \
      sudo apt-get install -y ros-jazzy-isaac-ros-rtdetr-models-install
   

3. Download and set up (convert ONNX to TensorRT engine plan) the pre-trained SyntheticaDETR model:

   sudo apt-get update
   

   ros2 run isaac_ros_rtdetr_models_install install_rtdetr_models.sh --eula
   

   Note

   This quickstart uses the NVIDIA-produced sdetr_grasp SyntheticaDETR model, but Isaac ROS RT-DETR is compatible with all RT-DETR architecture models. For more about the differences between SyntheticaDETR and RT-DETR, see here.

   Note

   The time taken to convert the ONNX model to a TensorRT engine plan varies across different platforms. On Jetson AGX Thor, for example, the engine conversion process can take up to 10-15 minutes to complete.

Run Launch File

RosbagRealSense CameraZED Camera

1. Continuing inside the Isaac ROS environment, install the following dependencies:

   sudo apt-get update
   

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

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

   ros2 launch isaac_ros_examples isaac_ros_examples.launch.py launch_fragments:=rtdetr interface_specs_file:=${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_rtdetr/quickstart_interface_specs.json engine_file_path:=${ISAAC_ROS_WS}/isaac_ros_assets/models/synthetica_detr/sdetr_grasp.plan
   

3. Open a second terminal inside the Isaac ROS environment:

   isaac-ros activate
   

4. Run the rosbag file to simulate an image stream:

   ros2 bag play -l ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_rtdetr/quickstart.bag
   

Visualize Results

1. Open a new terminal inside the Isaac ROS environment:

   isaac-ros activate
   

2. Run the RT-DETR visualization script:

   ros2 run isaac_ros_rtdetr isaac_ros_rtdetr_visualizer.py
   

3. Open another terminal inside the Isaac ROS environment:

   isaac-ros activate
   

4. Visualize and validate the output of the package with rqt_image_view:

   ros2 run rqt_image_view rqt_image_view /rtdetr_processed_image
   

   After about 1 minute, your output should look like this:

   

Try More Examples

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

• Tutorial with Isaac Sim

This package only supports models based on the RT-DETR architecture. Some of the supported RT-DETR models from NGC:

Model Name	Use Case
sdetr_grasp	Model trained on 100% synthetic data for object classes that can be grasped by a standard robot arm
sdetr_amr	Model trained on 100% synthetic data for object classes that are relevant to the operation of an Autonomous Mobile Robot

To learn how to use these models, click here.

Troubleshooting

Isaac ROS Troubleshooting

For solutions to problems with Isaac ROS, see troubleshooting.

Deep Learning Troubleshooting

For solutions to problems with using DNN models, see troubleshooting deeplearning.

API

Usage

ros2 launch isaac_ros_rtdetr isaac_ros_rtdetr.launch.py model_file_path:=<path to .onnx> engine_file_path:=<path to .plan> input_tensor_names:=<input tensor names> input_binding_names:=<input binding names> output_tensor_names:=<output tensor names> output_binding_names:=<output binding names> verbose:=<TensorRT verbosity> force_engine_update:=<force TensorRT update>

RtDetrPreprocessorNode

ROS Parameters

ROS Parameter	Type	Default	Description
input_image_tensor_name	string	input_tensor	The name of the encoded image tensor binding in the input tensor list.
output_image_tensor_name	string	images	The name of the encoded image tensor binding in the output tensor list.
output_size_tensor_name	string	orig_target_sizes	The name of the target image size tensor binding in the output tensor list.
image_height	int	480	The height of the original image, for resizing the final bounding box to match the original dimensions.
image_width	int	640	The width of the original image, for resizing the final bounding box to match the original dimensions.

ROS Topics Subscribed

ROS Topic	Interface	Description
encoded_tensor	isaac_ros_tensor_list_interfaces/TensorList	The tensor that contains the encoded image data.

ROS Topics Published

ROS Topic	Interface	Description
tensor_pub	isaac_ros_tensor_list_interfaces/TensorList	Tensor list containing encoded image data and image size tensors.

RtDetrDecoderNode

ROS Parameters

ROS Parameter	Type	Default	Description
labels_tensor_name	string	labels	The name of the labels tensor binding in the input tensor list.
boxes_tensor_name	string	boxes	The name of the boxes tensor binding in the input tensor list.
scores_tensor_name	string	scores	The name of the scores tensor binding in the input tensor list.
confidence_threshold	double	0.9	The minimum score required for a particular bounding box to be published.

ROS Topics Subscribed

ROS Topic	Interface	Description
tensor_sub	isaac_ros_tensor_list_interfaces/TensorList	The tensor that represents the inferred aligned bounding boxes, labels, and scores.

ROS Topics Published

ROS Topic	Interface	Description
detections_output	vision_msgs/Detection2DArray	Aligned image bounding boxes with detection class