isaac_ros_centerpose

Source code available on GitHub.

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_centerpose"
   NGC_RESOURCE="isaac_ros_centerpose_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
   

2. Download the CenterPose shoe ONNX file to the models repository directory with version 1:

mkdir -p ${ISAAC_ROS_WS}/isaac_ros_assets/models/centerpose_shoe/1 && \
  cd ${ISAAC_ROS_WS}/isaac_ros_assets/models/centerpose_shoe/1 && \
  wget --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/centerpose/deployable_dla34/files?redirect=true&path=shoe_DLA34.onnx' -O model.onnx

1. Move the quickstart model configuration file to the model repository.

   cp ${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_centerpose/config.pbtxt ${ISAAC_ROS_WS}/isaac_ros_assets/models/centerpose_shoe/config.pbtxt
   

   Warning

   The name within the configuration file must match the model repository name. Please look at the quickstart configuration file and modify it the goal is to run another model.

Build isaac_ros_centerpose

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-centerpose
   

Run Launch File

1. Continuing inside the Isaac ROS environment, convert the onnx model to a TensorRT engine plan:

   /usr/src/tensorrt/bin/trtexec --onnx=${ISAAC_ROS_WS}/isaac_ros_assets/models/centerpose_shoe/1/model.onnx --saveEngine=${ISAAC_ROS_WS}/isaac_ros_assets/models/centerpose_shoe/1/model.plan
   

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:=centerpose,centerpose_visualizer interface_specs_file:=${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_centerpose/quickstart_interface_specs.json model_name:=centerpose_shoe model_repository_paths:=[${ISAAC_ROS_WS}/isaac_ros_assets/models]
   

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_centerpose/quickstart.bag
   

Visualize Results

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

   isaac-ros activate
   

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

   ros2 run rqt_image_view rqt_image_view /centerpose/image_visualized
   

   After about 1 minute, your output should like this:

   

Use Different Models

NGC has CenterPose class models that can detect other objects. Check them out 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, please check here.

API

Usage

Two launch files are provided to use this package. The first launch file launches isaac_ros_tensor_rt, whereas the other one uses isaac_ros_triton, along with the necessary components to perform encoding on images and decoding of the CenterPose network’s output.

Warning

For your specific application, these launch files may need to be modified. Please consult the available components to see the configurable parameters.

Launch File	Components Used
isaac_ros_centerpose_tensor_rt.launch.py	DnnImageEncoderNode, TensorRTNode, CenterPoseDecoderNode, CenterPoseVisualizerNode
isaac_ros_centerpose_triton.launch.py	DnnImageEncoderNode, TritonNode, CenterPoseDecoderNode, CenterPoseVisualizerNode

CenterPoseDecoderNode

ROS Parameters

ROS Parameter	Type	Default	Description
output_field_size	int list	[128, 128]	An array of two integers that represent the size of the 2D keypoint decoding from the network output.
cuboid_scaling_factor	float	1.0	This parameter is used to scale the cuboid used for calculating the size of the objects detected.
score_threshold	float	0.3	The threshold for scores values to discard. Any score less than this value will be discarded.
object_name	string	shoe	The name of the category instance / object detected.

ROS Topics Subscribed

ROS Topic	Interface	Description
tensor_sub	isaac_ros_tensor_list_interfaces/TensorList	The TensorList that contains the outputs of the CenterPose network.
camera_info	sensor_msgs/CameraInfo	The CameraInfo of the original, undistorted image.

ROS Topics Published

ROS Topic	Interface	Description
centerpose/detections	vision_msgs/Detection3DArray	A Detection3DArray representing the poses of objects detected by the CenterPose network and interpreted by the CenterPose decoder node.

CenterPoseVisualizerNode

ROS Parameters

ROS Parameter	Type	Default	Description
show_axes	bool	true	Whether to draw the axes of the detected pose or not.
bounding_box_color	int32_t	0x000000ff	The color of the bounding box drawn. Only the last 24 bits are used to draw the color.

ROS Topics Subscribed

ROS Topic	Interface	Description
image	sensor_msgs/Image	The original, undistorted image.
camera_info	sensor_msgs/CameraInfo	The CameraInfo of the original, undistorted image.
centerpose/detections	vision_msgs/Detection3DArray	The detections made by the CenterPose decoder node.

ROS Topics Published

ROS Topic	Interface	Description
centerpose/image_visualized	sensor_msgs/Image	An image containing the detection’s bounding box reprojected onto the image and, optionally, the axes of the detected objects.