diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..a89cd42
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,14 @@
+# Isaac ROS Contribution Rules
+
+Any contribution that you make to this repository will
+be under the Apache 2 License, as dictated by that
+[license](http://www.apache.org/licenses/LICENSE-2.0.html):
+
+> **5. Submission of Contributions.** Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
+
+Contributors must sign-off each commit by adding a `Signed-off-by: ...`
+line to commit messages to certify that they have the right to submit
+the code they are contributing to the project according to the
+[Developer Certificate of Origin (DCO)](https://developercertificate.org/).
+
+[//]: # (202201002)
diff --git a/LICENSE b/LICENSE
index 88bc900..7a4a3ea 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,66 +1,202 @@
-NVIDIA ISAAC ROS SOFTWARE LICENSE
-This license is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs the use of the NVIDIA Isaac ROS software and materials provided hereunder (“SOFTWARE”).
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diff --git a/README.md b/README.md
index cef7a46..77c164e 100644
--- a/README.md
+++ b/README.md
@@ -3,20 +3,23 @@
40ms | N/A | 84 fps
15.4ms |
+These data have been collected per the methodology described [here](https://github.com/NVIDIA-ISAAC-ROS/.github/blob/main/profile/performance-summary.md#methodology).
## Table of Contents
+
- [Isaac ROS Pose Estimation](#isaac-ros-pose-estimation)
- [Overview](#overview)
- [Performance](#performance)
@@ -49,27 +52,28 @@ The following are the benchmark performance results of the prepared pipelines in
- [Updates](#updates)
## Latest Update
-Update 2022-08-31: Update to accelerate DOPE with [NVIDIA Isaac Transport for ROS (NITROS)](https://developer.nvidia.com/blog/improve-perception-performance-for-ros-2-applications-with-nvidia-isaac-transport-for-ros/) and to be compatible with JetPack 5.0.2
+
+Update 2022-10-19: Updated OSS licensing
## Supported Platforms
+
This package is designed and tested to be compatible with ROS2 Humble running on [Jetson](https://developer.nvidia.com/embedded-computing) or an x86_64 system with an NVIDIA GPU.
> **Note**: Versions of ROS2 earlier than Humble are **not** supported. This package depends on specific ROS2 implementation features that were only introduced beginning with the Humble release.
-
-| Platform | Hardware | Software | Notes |
-| -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Jetson | [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/)
[Jetson Xavier](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/) | [JetPack 5.0.2](https://developer.nvidia.com/embedded/jetpack) | For best performance, ensure that [power settings](https://docs.nvidia.com/jetson/archives/r34.1/DeveloperGuide/text/SD/PlatformPowerAndPerformance.html) are configured appropriately. |
-| x86_64 | NVIDIA GPU | [Ubuntu 20.04+](https://releases.ubuntu.com/20.04/)
[CUDA 11.6.1+](https://developer.nvidia.com/cuda-downloads) |
-
+| Platform | Hardware | Software | Notes |
+| -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Jetson | [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/)
[Jetson Xavier](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/) | [JetPack 5.0.2](https://developer.nvidia.com/embedded/jetpack) | For best performance, ensure that [power settings](https://docs.nvidia.com/jetson/archives/r34.1/DeveloperGuide/text/SD/PlatformPowerAndPerformance.html) are configured appropriately. |
+| x86_64 | NVIDIA GPU | [Ubuntu 20.04+](https://releases.ubuntu.com/20.04/)
[CUDA 11.6.1+](https://developer.nvidia.com/cuda-downloads) |
### Docker
+
To simplify development, we strongly recommend leveraging the Isaac ROS Dev Docker images by following [these steps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/docs/dev-env-setup.md). This will streamline your development environment setup with the correct versions of dependencies on both Jetson and x86_64 platforms.
> **Note:** All Isaac ROS Quickstarts, tutorials, and examples have been designed with the Isaac ROS Docker images as a prerequisite.
-
## Quickstart
+
> **Warning**: Step 7 must be performed on `x86_64`. The resultant model should be copied over to the `Jetson`. Also note that the process of model preparation differs significantly from the other repositories.
1. Set up your development environment by following the instructions [here](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common/blob/main/docs/dev-env-setup.md).
@@ -103,20 +107,25 @@ To simplify development, we strongly recommend leveraging the Isaac ROS Dev Dock
```
4. Launch the Docker container using the `run_dev.sh` script:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
5. Make a directory to place models (inside the Docker container):
+
```bash
mkdir -p /tmp/models/
```
-6. Select a DOPE model by visiting the DOPE model collection available on the official [DOPE GitHub](https://github.com/NVlabs/Deep_Object_Pose) repository [here](https://drive.google.com/open?id=1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg). The model is assumed to be downloaded to `~/Downloads` outside the Docker container.
-
+
+6. Select a DOPE model by visiting the DOPE model collection available on the official [DOPE GitHub](https://github.com/NVlabs/Deep_Object_Pose) repository [here](https://drive.google.com/open?id=1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg). The model is assumed to be downloaded to `~/Downloads` outside the Docker container.
+
This example will use `Ketchup.pth`, which should be downloaded into `/tmp/models` inside the Docker container:
> **Note**: this should be run outside the Docker container
-
+
On `x86_64`:
+
```bash
cd ~/Downloads && \
docker cp Ketchup.pth isaac_ros_dev-x86_64-container:/tmp/models
@@ -129,48 +138,59 @@ To simplify development, we strongly recommend leveraging the Isaac ROS Dev Dock
python3 /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py --format onnx --input /tmp/models/Ketchup.pth
```
- If you are planning on using Jetson, copy the generated `.onnx` model into the Jetson, and then copy it over into `aarch64` Docker container.
-
+ If you are planning on using Jetson, copy the generated `.onnx` model into the Jetson, and then copy it over into `aarch64` Docker container.
+
We will assume that you already performed the transfer of the model onto the Jetson in the directory `~/Downloads`.
Enter the Docker container in Jetson:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
Make a directory called `/tmp/models` in Jetson:
+
```bash
mkdir -p /tmp/models
```
**Outside** the container, copy the generated `onnx` model:
+
```bash
cd ~/Downloads && \
docker cp Ketchup.onnx isaac_ros_dev-aarch64-container:/tmp/models
```
-8. Inside the container, build and source the workspace:
+8. Inside the container, build and source the workspace:
+
```bash
cd /workspaces/isaac_ros-dev && \
colcon build --symlink-install && \
source install/setup.bash
```
-9. (Optional) Run tests to verify complete and correct installation:
+
+9. (Optional) Run tests to verify complete and correct installation:
+
```bash
colcon test --executor sequential
```
+
10. Run the following launch files to spin up a demo of this package:
-
+
Launch `isaac_ros_dope`:
+
```bash
ros2 launch isaac_ros_dope isaac_ros_dope_tensor_rt.launch.py model_file_path:=/tmp/models/Ketchup.onnx engine_file_path:=/tmp/models/Ketchup.plan
```
+
Then open **another** terminal, and enter the Docker container again:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
Then, play the ROS bag:
```bash
@@ -178,21 +198,26 @@ To simplify development, we strongly recommend leveraging the Isaac ROS Dev Dock
```
11. Open another terminal window and attach to the same container. You should be able to get the poses of the objects in the images through `ros2 topic echo`:
-
+
In a **third** terminal, enter the Docker container again:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
```bash
ros2 topic echo /poses
```
+
> **Note**: We are echoing `/poses` because we remapped the original topic `/dope/pose_array` to `poses` in the launch file.
Now visualize the pose array in rviz2:
+
```bash
rviz2
```
+
Then click on the `Add` button, select `By topic` and choose `PoseArray` under `/poses`. Finally, change the display to show an axes by updating `Shape` to be `Axes`, as shown in the screenshot below. Make sure to update the `Fixed Frame` to `camera`.
+
+## Overview
+
+This tutorial walks you through a pipeline to estimate the 6DOF pose of a target object using [CenterPose](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation) with Triton. It uses input monocular images from a rosbag.
> **Warning**: These steps will only work on `x86_64` and **NOT** on `Jetson`.
+## Tutorial Walkthrough
+
1. Complete steps 1-5 of the quickstart [here](../README.md#quickstart)
2. Select a CenterPose model by visiting the CenterPose model collection available on the official [CenterPose GitHub](https://github.com/NVlabs/CenterPose) repository [here](https://drive.google.com/drive/folders/1QIxcfKepOR4aktOz62p3Qag0Fhm0LVa0). The model is assumed to be downloaded to `~/Downloads` outside the docker container. This example will use `shoe_resnet_140.pth`, which should be downloaded into `/tmp/models` inside the docker container:
> **Note**: this should be run outside the container
+
```bash
cd ~/Downloads && \
docker cp shoe_resnet_140.pth isaac_ros_dev-x86_64-container:/tmp/models
@@ -13,59 +21,68 @@ This tutorial is for using CenterPose with Triton.
> **Warning**: The models in the root directory of the model collection listed above will *NOT WORK* with our inference nodes because they have custom layers not supported by TensorRT nor Triton. Make sure to use the PyTorch weights that have the string `resnet` in their file names.
3. Create a models repository with version `1`:
+
```bash
mkdir -p /tmp/models/centerpose_shoe/1
```
4. Create a configuration file for this model at path `/tmp/models/centerpose_shoe/config.pbtxt`. Note that name has to be the same as the model repository name. Take a look at the example at `isaac_ros_centerpose/test/models/centerpose_shoe/config.pbtxt` and copy that file to `/tmp/models/centerpose_shoe/config.pbtxt`.
+
```bash
cp /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_centerpose/test/models/centerpose_shoe/config.pbtxt /tmp/models/centerpose_shoe/config.pbtxt
```
-5. To run the TensorRT engine plan, convert the PyTorch model to ONNX first. Export the model into an ONNX file using the script provided under `/workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_centerpose/scripts/centerpose_pytorch2onnx.py`:
+5. To run the TensorRT engine plan, convert the PyTorch model to ONNX first. Export the model into an ONNX file using the script provided under `/workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_centerpose/scripts/centerpose_pytorch2onnx.py`:
+
```bash
python3 /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_centerpose/scripts/centerpose_pytorch2onnx.py --input /tmp/models/shoe_resnet_140.pth --output /tmp/models/centerpose_shoe/1/model.onnx
```
-6. To get a TensorRT engine plan file with Triton, export the ONNX model into an TensorRT engine plan file using the builtin TensorRT converter `trtexec`:
+
+6. To get a TensorRT engine plan file with Triton, export the ONNX model into an TensorRT engine plan file using the builtin TensorRT converter `trtexec`:
+
```bash
/usr/src/tensorrt/bin/trtexec --onnx=/tmp/models/centerpose_shoe/1/model.onnx --saveEngine=/tmp/models/centerpose_shoe/1/model.plan
```
-7. Inside the container, build and source the workspace:
+7. Inside the container, build and source the workspace:
+
```bash
cd /workspaces/isaac_ros-dev && \
colcon build --symlink-install && \
source install/setup.bash
```
-8. Start `isaac_ros_centerpose` using the launch file:
+8. Start `isaac_ros_centerpose` using the launch file:
+
```bash
ros2 launch isaac_ros_centerpose isaac_ros_centerpose.launch.py model_name:=centerpose_shoe model_repository_paths:=['/tmp/models']
```
Then open **another** terminal, and enter the Docker container again:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
Then, play the ROS bag:
-
+
```bash
ros2 bag play -l src/isaac_ros_pose_estimation/resources/rosbags/centerpose_rosbag/
```
-9. Open another terminal window and attach to the same container. You should be able to get the poses of the objects in the images through `ros2 topic echo`:
-
+9. Open another terminal window and attach to the same container. You should be able to get the poses of the objects in the images through `ros2 topic echo`:
+
In a **third** terminal, enter the Docker container again:
+
```bash
cd ~/workspaces/isaac_ros-dev/src/isaac_ros_common && \
./scripts/run_dev.sh
```
+
```bash
source install/setup.bash && \
ros2 topic echo /object_poses
```
10. Launch `rviz2`. Click on `Add` button, select "By topic", and choose `MarkerArray` under `/object_poses`. Set the fixed frame to `centerpose`. You'll be able to see the cuboid marker representing the object's pose detected!
-
-