`isaac_ros_centerpose`

Source code on GitHub.

Quickstart

Set Up Development Environment

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

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

(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

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=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

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

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`

Launch the Docker container using the run_dev.sh script:

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

Install the prebuilt Debian package:

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

Clone this repository under ${ISAAC_ROS_WS}/src:

cd ${ISAAC_ROS_WS}/src && \
   git clone -b release-3.1 https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation.git isaac_ros_pose_estimation

Launch the Docker container using the run_dev.sh script:

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

Use rosdep to install the package’s dependencies:

rosdep install --from-paths ${ISAAC_ROS_WS}/src/isaac_ros_pose_estimation --ignore-src -y

Build the package from source:

cd ${ISAAC_ROS_WS} && \
   colcon build --symlink-install --packages-up-to isaac_ros_centerpose

Source the ROS workspace:

Note

Make sure to repeat this step in every terminal created inside the Docker container.

Because this package was built from source, the enclosing workspace must be sourced for ROS to be able to find the package’s contents.
```
source install/setup.bash
```

Run Launch File

Continuing inside the Docker container, 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

Continuing inside the Docker container, install the following dependencies:
```
sudo apt-get install -y ros-humble-isaac-ros-examples
```

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]

Open a second terminal inside the Docker container:

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

Run the rosbag file to simulate an image stream:

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

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.

Continuing inside the Docker container, install the following dependencies:

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

Run the following launch file to spin up a demo of this package using a RealSense camera:

ros2 launch isaac_ros_examples isaac_ros_examples.launch.py launch_fragments:=realsense_mono_rect,centerpose,centerpose_visualizer model_name:=centerpose_shoe model_repository_paths:=[${ISAAC_ROS_WS}/isaac_ros_assets/models]

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

Continuing inside the Docker container, install the following dependencies:

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

Run the following launch file to spin up a demo of this package using a Hawk camera:

ros2 launch isaac_ros_examples isaac_ros_examples.launch.py launch_fragments:=argus_mono,rectify_mono,centerpose,centerpose_visualizer model_name:=centerpose_shoe model_repository_paths:=[${ISAAC_ROS_WS}/isaac_ros_assets/models]

Ensure that you have already set up your ZED camera using ZED setup tutorial.

Continuing inside the Docker container, install dependencies:

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

Run the following launch file to spin up a demo of this package using a ZED Camera:

ros2 launch isaac_ros_examples isaac_ros_examples.launch.py \
launch_fragments:=zed_mono_rect,centerpose,centerpose_visualizer model_name:=centerpose_shoe model_repository_paths:=[${ISAAC_ROS_WS}/isaac_ros_assets/models] \
interface_specs_file:=${ISAAC_ROS_WS}/isaac_ros_assets/isaac_ros_centerpose/zed2_quickstart_interface_specs.json

Note

If you are using the ZED X series, replace zed2_quickstart_interface_specs.json with zedx_quickstart_interface_specs.json in the above command.

Visualize Results

Open a new terminal inside the Docker container:

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

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.

isaac_ros_centerpose

Quickstart

Set Up Development Environment

Download Quickstart Assets

Build isaac_ros_centerpose

Run Launch File

Visualize Results

Use Different Models

Troubleshooting

Isaac ROS Troubleshooting

Deep Learning Troubleshooting

API

Usage

CenterPoseDecoderNode

ROS Parameters

ROS Topics Subscribed

ROS Topics Published

CenterPoseVisualizerNode

ROS Parameters

ROS Topics Subscribed

ROS Topics Published

`isaac_ros_centerpose`

Build `isaac_ros_centerpose`