isaac_ros_rtdetr

Source code 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. Clone isaac_ros_common under ${ISAAC_ROS_WS}/src.

   cd ${ISAAC_ROS_WS}/src && \
      git clone -b release-3.1 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.

   Warning

   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_rtdetr"
   NGC_RESOURCE="isaac_ros_rtdetr_assets"
   NGC_FILENAME="quickstart.tar.gz"
   MAJOR_VERSION=3
   MINOR_VERSION=1
   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
   

2. Download a pre-trained SyntheticaDETR model to use in the quickstart:

   mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets/models/synthetica_detr && \
   cd ${ISAAC_ROS_WS}/isaac_ros_assets/models/synthetica_detr && \
      wget 'https://api.ngc.nvidia.com/v2/models/nvidia/isaac/synthetica_detr/versions/1.0.0/files/sdetr_grasp.etlt'
   

   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.

Build isaac_ros_rtdetr

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 install -y ros-humble-isaac-ros-rtdetr
   

Run Launch File

1. Continuing inside the Docker container, convert the encrypted model (.etlt) to a TensorRT engine plan:

   /opt/nvidia/tao/tao-converter -k sdetr -t fp16 -e ${ISAAC_ROS_WS}/isaac_ros_assets/models/synthetica_detr/sdetr_grasp.plan -p images,1x3x640x640,2x3x640x640,4x3x640x640 -p orig_target_sizes,1x2,2x2,4x2 ${ISAAC_ROS_WS}/isaac_ros_assets/models/synthetica_detr/sdetr_grasp.etlt
   

   Note

   The model conversion time varies across different platforms. On Jetson AGX Orin, for example, the engine conversion process can take up to 10-15 minutes to complete.

RosbagRealSense CameraHawk CameraZED Camera

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

   sudo apt-get install -y ros-humble-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 Docker container:

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

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 Docker container:

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

2. Run the RT-DETR visualization script:

   ros2 run isaac_ros_rtdetr isaac_ros_rtdetr_visualizer.py
   

3. Open another terminal inside the Docker container:

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

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