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Jetson ORIN NANO SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notesnew
  • 04. Disable automatic updates and troubleshooting issues
  • 05. Notes on Jetson Orin Nano Official Kitnew
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Write Jetson original system
  • 03.Installing Jetson environment
  • 04.Backup SSD system
  • 05.Restore initial image system
  • 06.Write SUPER original system(optional)
  • 07.Write SUPER boot (optional)
  • 08. SSD expansion
  • 09.Jetpack old system upgraded to Yahboom SUPER factory system
  • 10.Write the system onto your own solid-state drive
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.Vision basic course
  • 01.CSI camera preview
  • 02.USB camera preview
  • 03.Use Jupyter Lab
  • 04.Use JetCam
06.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
  • 17.Preview USB camera image
  • 18.Preview CSI camera image
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • QR code recognition
06.YOLO26 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detectionnew
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS1-Melodic
01.ROS1 basic course
  • 01. Introduction to ROS
  • 02. Project file structure
  • 03. Common commands and tools
  • 04. Publisher
  • 05. Subscribers
  • 06. Customize topic messages and use
  • 07. Client
  • 08. Server
  • 09. Customize service messages and usage
  • 10. TF release and monitoring
02.ROS1 vision course
  • 00.Must read before running
  • 01.Camera preview
  • 02.QR code
  • 03.Human posture estimation
  • 04.Target detection
  • 05.AR vision
  • 06.Opencv application
  • 07.ROS+Opencv basics
  • 08.Mediapipe development
10.ROS2-Humble
01.ROS2 basic course
  • 01.Introduction to ROS2
  • 02.ROS2 install Humble
  • 03.ROS2 development environment
  • 04.ROS2 workspace
  • 05.ROS2 function package
  • 06.ROS2 node
  • 07.ROS2 topic communication
  • 08.ROS2 service communication
  • 09.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera preview (CSI)
  • 03.Camera calibration (USB)
  • 04.Camera calibration (CSI)
  • 05.AR vision (USB)
  • 06.AR vision (CSI)
11.Offline AI model coursenew
Offline large model (Text Interaction)
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
12.Online AI model coursenew
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
13.NVIDIA lsaac ROS coursenew
  • 1.Build lsaac ROS environment
  • 2.Deep segmentation
  • 3.DNN stereoscopic depth
  • 4.Free space segmentation
  • 5.Image distortion processing
  • 6.Image segmentation
  • 7.3D scene reconstruction and mapping
  • 8.Object detection
  • 9.3D attitude estimation
  • 10.Visual SLAM
14.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start on Jetson Platform
  • 02.OpenClaw Deployment
  • 03.OpenClaw WhatsApp Plugin Application
  • 04.OpenClaw WebChat Interaction
  • 05.OpenClaw TUI Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawHub Introduction (Skill Installation)
  • 10.OpenClaw Application - File Management
  • 11.OpenClaw Application - Camera
  • 12.OpenClaw Application - Browser Control
  • 13.OpenClaw Application - Script Execution
  • 14.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 15.OpenClaw Application - Dedicated AI Assistant
  • 16.OpenClaw Web Search Capability Configuration
15.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 01. Peripheral Hardware Configuration
  • 02. OpenClaw API-KEY Configuration
  • 03. OpenClaw Model Switching
  • 04. OpenClaw Prompts
  • 05. AI Voice Interaction Configuration
  • 06. Three Dialogue Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
Download
  • System_File
  • VM_File
  • 3D_Model_File
  • DataSheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
Download ZIP

Welcome to Jetson ORIN NANO SUPER repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

06. Customize topic messages and use

6.Customized topic messages and usage

6.1 Customized topic messages

Switch to~/catkin_ ws/src/learning_ Create a new folder named msg under the topic feature pack directory to store custom topic messages.

6.1.1. Define msg files

Switch to the msg directory and create a new blank msg file, with the suffix "msg" indicating that it is an msg file. Here, we use Information.msg as an example to copy the following code into the newly created msg file.

string company
string city  

6.1.2. Add feature package dependencies in package.xml

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<build_depend>message_generation</build_depend>
<exec_depend>message_runtime</exec_depend>

6.1.3. Add compilation options in CMakeLists.txt

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add_message_files(FILES Information.msg)
generate_messages(DEPENDENCIES std_msgs)

6.1.4 Compile and generate language related files

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cd ~/catkin_ws
catkin_make

6.1.5. C++Language Implementation

  1. Switch to~/catkin_ ws/src/learning_ Under topic/src, create two new cpp files named Information_ Publisher.cpp and Information_ Subscriber.cpp, copy the following code into it separately,Information_ publisher.cpp
x
#include <ros/ros.h>
#include "learning_topic/Information.h"

int main(int argc, char **argv)
{
    // ROS node initialization
    ros::init(argc, argv, "company_Information_publisher");

    // Create node handle
    ros::NodeHandle n;

    // Create a Publisher to publish a topic named/company info, with a message type of learning_topic:: Person and a queue length of 10
    ros::Publisher Information_pub = n.advertise<learning_topic::Information>("/company_info", 10);

    // Set the frequency of the loop
    ros::Rate loop_rate(1);

    int count = 0;
    while (ros::ok())
    {
        // Initialize learning_topic:: Information type message
        learning_topic::Information info_msg;
        info_msg.company = "Yahboom";
        info_msg.city = "Shenzhen";

        // Publish Message
        Information_pub.publish(info_msg);

        ROS_INFO("Information: company:%s  city:%s ", 
                  info_msg.company.c_str(), info_msg.city.c_str());

        
        loop_rate.sleep();// Delay according to the cycle frequency
    }

    return 0;
}

Information_subscriber.cpp

x
#include <ros/ros.h>
#include "learning_topic/Information.h"

// After receiving the subscription message, it will enter the message callback function to process the data
void CompanyInfoCallback(const learning_topic::Information::ConstPtr& msg)
{
    // Print the received message
    ROS_INFO("This is: %s  in %s", msg->company.c_str(), msg->city.c_str());
}

int main(int argc, char **argv)
{
    
    ros::init(argc, argv, "company_Information_subscriber");//Initialize ROS nodes

    
    ros::NodeHandle n;// This is creating a node handle

    // Create a subscriber, subscribe to the topic/company info, and register the callback function Company Infocallback
    ros::Subscriber person_info_sub = n.subscribe("/company_info", 10, CompanyInfoCallback);

    
    ros::spin();// Loop waiting callback function

    return 0;
}
  1. Modify the CMakeLists.txt file
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add_executable(Information_publisher src/Information_publisher.cpp)
target_link_libraries(Information_publisher ${catkin_LIBRARIES})
add_dependencies(Information_publisher ${PROJECT_NAME}_generate_messages_cpp)

add_executable(Information_subscriber src/Information_subscriber.cpp)
target_link_libraries(Information_subscriber ${catkin_LIBRARIES})
add_dependencies(Information_subscriber ${PROJECT_NAME}_generate_messages_cpp)
  1. Core part

The implementation process here is the same as before, with the main difference being the introduction of header files and the use of custom message files:The import header file is

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#include "learning_topic/Information.h"

Front learning_ Topic is the name of the feature pack, followed by Information. h, which is the header file name generated by the previously created msg fileUsing a custom message file is

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learning_topic::Information info_msg;
info_msg.company = "Yahboom";
info_msg.city = "Shenzhen";
void CompanyInfoCallback(const learning_topic::Information::ConstPtr& msg)
  1. Run program
xxxxxxxxxx
roscore
rosrun learning_topic Information_publisher
rosrun learning_topic Information_subscriber
  1. Run screenshot

  1. Program Description

Information_ As a publisher, publisher continuously posts messages to the topic of "/company_info" and prints the published messages; As a subscriber, Information_ The subscriber also continuously receives the content of the topic "/company_info" and prints it out in the callback function.

6.1.6 Python Language Implementation

  1. Switch to~/catkin_ ws/src/learning_ Under topic/script, create two new py files and name them Information_ Publisher.py and Information_ Subscriber.py, copy the following code into it separately,Information_ publisher.py
x
#!/usr/bin/env python
# -*- coding: utf-8 -*-


import rospy

from learning_topic.msg import Information #Import custom msg

def information_publisher():
    
    rospy.init_node('information_publisher', anonymous=True)# ROS node initialization

    info_pub = rospy.Publisher('/company_info', Information, queue_size=6)

    rate = rospy.Rate(10) #Set the frequency of the loop

    while not rospy.is_shutdown():

    # Initialize learning_topic:: Information type message
        info_msg = Information()
        info_msg.company = "Yahboom";
        info_msg.city  = "Shenzhen";
    
        info_pub.publish(info_msg)# Publish Message

        rospy.loginfo("This is %s in %s.", info_msg.company, info_msg.city)# Print and publish messages
        
        rate.sleep()# Delay according to the cycle frequency

if __name__ == '__main__':
    try:
        information_publisher()
    except rospy.ROSInterruptException:
        pass

Information_subscriber.py

x
#!/usr/bin/env python
# -*- coding: utf-8 -*-


import rospy

from learning_topic.msg import Information #Import custom msg

def CompanyInfoCallback(msg):

    rospy.loginfo("company: name:%s  city:%s ", msg.company, msg.city)#Print subscription received information

def Infomation_subscriber():
    
    rospy.init_node('Infomation_subscriber', anonymous=True)# ROS node initialization

    # Create a subscriber to subscribe to a topic named/company info and register the callback function personInfocallbacks
    rospy.Subscriber("/company_info", Information, CompanyInfoCallback)

    rospy.spin()# Loop waiting callback function

if __name__ == '__main__':
    Infomation_subscriber()

  1. Core part

Here is mainly an explanation of how to import custom message modules and how to use them:Import

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from learning_topic.msg import Information

use

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info_msg = Person()
info_msg.company = "Yahboom";
info_msg.city  = "Shenzhen";
  1. run a program

Before running the program, add executable permissions to the py file

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sudo chmod a+x Information_subscriber.py
sudo chmod a+x Information_publisher.py

run a program

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roscore
rosrun learning_topic Information_publisher.py
rosrun learning_topic Information_subscriber.py
  1. Run screenshot

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