Deep Learning Nodes for ROS/ROS2

This repo contains deep learning inference nodes and camera/video streaming nodes for ROS/ROS2 with support for Jetson Nano/TX1/TX2/Xavier NX/AGX Xavier and TensorRT.

The nodes use the image recognition, object detection, and semantic segmentation DNN's from the jetson-inference library and NVIDIA Hello AI World tutorial, which come with several built-in pretrained networks for classification, detection, and segmentation and the ability to load customized user-trained models.

The camera/video streaming nodes support the following input/output interfaces:

• MIPI CSI cameras
• V4L2 cameras
• RTP / RTSP
• Videos & Images
• Image sequences
• OpenGL windows

ROS Melodic and ROS2 Eloquent are supported, and the latest version of JetPack is recommended.

Table of Contents

• Installation
  • jetson-inference
  • ROS/ROS2
  • ros_deep_learning
• Testing
  • Video Viewer
  • imagenet Node
  • detectnet Node
  • segnet Node
• Topics & Parameters
  • imagenet Node
  • detectnet Node
  • segnet Node
  • video_source Node
  • video_output Node

Installation

First, install the latest version of JetPack on your Jetson.

Then, follow the steps below to install the needed components on your Jetson.

jetson-inference

These ROS nodes use the DNN objects from the jetson-inference project (aka Hello AI World). To build and install jetson-inference, see this page or run the commands below:

$ cd ~
$ sudo apt-get install git cmake
$ git clone --recursive https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
$ mkdir build
$ cd build
$ cmake ../
$ make -j$(nproc)
$ sudo make install
$ sudo ldconfig

Before proceeding, it's worthwhile to test that jetson-inference is working properly on your system by following this step of the Hello AI World tutorial:

• Classifying Images with ImageNet

ROS/ROS2

Install the ros-melodic-ros-base or ros-eloquent-ros-base package on your Jetson following these directions:

• ROS Melodic - ROS Install Instructions
• ROS2 Eloquent - ROS2 Install Instructions

Depending on which version of ROS you're using, install some additional dependencies and create a workspace:

ROS Melodic

$ sudo apt-get install ros-melodic-image-transport ros-melodic-vision-msgs

For ROS Melodic, create a Catkin workspace (~/ros_workspace) using these steps:
http://wiki.ros.org/ROS/Tutorials/InstallingandConfiguringROSEnvironment#Create_a_ROS_Workspace

ROS Eloquent

$ sudo apt-get install ros-eloquent-vision-msgs \
                       ros-eloquent-launch-xml \
                       ros-eloquent-launch-yaml \
                       python3-colcon-common-extensions

For ROS Eloquent, create a workspace (~/ros_workspace) to use:

$ mkdir -p ~/ros2_example_ws/src

ros_deep_learning

Next, navigate into your ROS workspace's src directory and clone ros_deep_learning:

$ cd ~/ros_workspace/src
$ git clone https://github.com/dusty-nv/ros_deep_learning

Then build it - if you are using ROS Melodic, use catkin_make. If you are using ROS2 Eloquent, use colcon build:

$ cd ~/ros_workspace/

# ROS Melodic
$ catkin_make
$ source devel/setup.bash 

# ROS2 Eloquent
$ colcon build
$ source install/local_setup.bash 

The nodes should now be built and ready to use. Remember to source the overlay as shown above so that ROS can find the nodes.

Testing

Before proceeding, if you're using ROS Melodic make sure that roscore is running first:

$ roscore

If you're using ROS2, running the core service is no longer required.

Video Viewer

First, it's recommended to test that you can stream a video feed using the video_source and video_output nodes. See Camera Streaming & Multimedia for valid input/output streams, and substitute your desired input and output argument below. For example, you can use video files for the input or output, or use V4L2 cameras instead of MIPI CSI cameras. You can also use RTP/RTSP streams over the network.

# ROS Melodic
$ roslaunch ros_deep_learning video_viewer.ros1.launch input:=csi://0 output:=display://0

# ROS2 Eloquent
$ ros2 launch ros_deep_learning video_viewer.ros2.launch input:=csi://0 output:=display://0

imagenet Node

You can launch a classification demo with the following commands - substitute your desired camera or video path to the input argument below (see here for valid input/output streams).

Note that the imagenet node also publishes classification metadata on the imagenet/classification topic in a vision_msgs/Detection2DArray message -- see the Topics & Parameters section below for more info.

# ROS Melodic
$ roslaunch ros_deep_learning imagenet.ros1.launch input:=csi://0 output:=display://0

# ROS2 Eloquent
$ ros2 launch ros_deep_learning imagenet.ros2.launch input:=csi://0 output:=display://0

detectnet Node

To launch an object detection demo, substitute your desired camera or video path to the input argument below (see here for valid input/output streams). Note that the detectnet node also publishes the metadata in a vision_msgs/Detection2DArray message -- see the Topics & Parameters section below for more info.

# ROS Melodic
$ roslaunch ros_deep_learning detectnet.ros1.launch input:=csi://0 output:=display://0

# ROS2 Eloquent
$ ros2 launch ros_deep_learning detectnet.ros2.launch input:=csi://0 output:=display://0

segnet Node

To launch a semantic segmentation demo, substitute your desired camera or video path to the input argument below (see here for valid input/output streams). Note that the segnet node also publishes raw segmentation results to the segnet/class_mask topic -- see the Topics & Parameters section below for more info.

# ROS Melodic
$ roslaunch ros_deep_learning segnet.ros1.launch input:=csi://0 output:=display://0

# ROS2 Eloquent
$ ros2 launch ros_deep_learning segnet.ros2.launch input:=csi://0 output:=display://0

Topics & Parameters

Below are the message topics and parameters that each node implements.

imagenet Node

detectnet Node

segnet Node

video_source Node

video_output Node