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Jetson AGX Thor📖
1.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
2.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flashing a Pure Jetson System via SDK
  • 03.Flashing a Pure Jetson System via USB Drive
  • 04.Flashing the Jetson Bootloader using L4T
  • 05.Replacing the SSD
  • 06.Install Jetpack environment
  • 07.Backup SSD system
  • 08.Restore factory image system
  • 09.SSD expansion
  • 10.SSD Speed Test
3.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
  • 10.Change screen resolution
  • 11.Virtual Desktop
  • 12.Set root user password
  • 13.Configure sudo passwordless operation
  • 14.Managing custom services
  • 15.View system version information
4.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
5.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
6.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
  • 04.Torchaudio
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO26 development
  • 01.YOLO Environment Setup
  • 02.CLI Usage
  • 03.Object Detection
  • 04.Instance Segmentation
  • 05.Pose Estimation
  • 06.Image Classification
  • 07.Oriented Bounding Boxes 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
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
7.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
8.ROS2-jazzy
1.ROS2 basic course
  • 1.Introduction to ROS2
  • 2.ROS2 install Jazzy
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.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
2.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
9.Offline AI model course
Offline large model (Text Interaction)
  • 1.Al 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
10.Online AI model course
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal Video Analysis Applications
  • 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 offline voice assistant
11.Generative AI Applications
12.OpenClaw Advanced Development
  • 00.OpenClaw Quick Start on Jetson Platform
  • 01.OpenClaw Installation
  • 02.OpenClaw Online Platform Deployment
  • 03.OpenClaw Local Model Deployment
  • 04.OpenClaw TUI Interaction
  • 05.OpenClaw WebChat Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10. OpenClaw Switching Models
  • 11.OpenClaw Application - File Management
  • 12.OpenClaw Application - Camera
  • 13.OpenClaw Application - Browser Control
  • 14.OpenClaw Application - Script Execution
  • 15.OpenClaw Application - AI Programming
  • 16.OpenClaw Application - Dedicated AI Assistant
  • 17.OpenClaw WhatsApp Plugin Application
  • 18.OpenClaw Voice Assistant
Download
  • Code
  • SystemFile
  • VM_File
  • Tools
  • Jetson boot files
Download ZIP

Welcome to Jetson AGX Thor repository

17.ROS2 rviz2 use

17. Using ROS2 Rviz2

1. Introduction to Rviz2

During robot development, various functions require analysis at the data level, making it difficult to quickly understand their impact. For example, for robot models, we need to understand the design's appearance and the positions of the model's numerous internal coordinate systems during motion.

For robotic arm motion planning and mobile robot autonomous navigation, we need to visualize the robot's surrounding environment, the planned path, and, of course, sensor information, such as cameras, 3D cameras, and lidar. Data is used for calculations, while visualization is for human viewing.

Therefore, data visualization can greatly improve development efficiency. Rviz2 is such a data visualization software for robot development. It can handle everything from robot models, sensor information, and environmental information.

2. Preparation

  • If you have a physical robot, you can launch Rviz on the robot controller to practice this lesson. If you don't have a physical robot, you can use Gazebo to simulate a TurtleBot3 robot to simulate the lidar, camera, and other components, facilitating subsequent data visualization.

Note: The following installation steps are optional. If you have a physical robot and have set up multi-machine communication, you can directly use the actual robot's radar information. You can choose to use the actual radar or a simulated robot. The following information is for users without a physical robot.

  • This lesson uses a simulated robot as an example to teach you the visualization capabilities of Rviz2. The operation process is the same for both real and simulated robots.
  • Install the TutleBot3 simulator package
xxxxxxxxxx
sudo apt install ros-${ROS_DISTRO}-turtlebot3*
  • Install the ROS and Gazebo bridge tool
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sudo apt install ros-${ROS_DISTRO}-ros-gz
  • Set the TurtleBot3 robot type environment variable
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export TURTLEBOT3_MODEL=waffle
  • Start the Gazebo simulation environment
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ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

image-20250905164250419

3. Starting Rviz2

Open a terminal and run the following command to start it:

xxxxxxxxxx
rviz2

If starting in Docker, make sure the GUI is enabled.

image-20231031104047357

4. Image Data Visualization

Click "Add" in the Displays window on the left, find the Image display item, and confirm it to add it to the display list. Then, configure the image topic to which the display item is subscribed. You can then view the robot's camera image.

image-20231031104244974

  • Select Fixed Frame as the base_footprint coordinate system, and place the coordinate transformation error.
  • Select the camera color image topic /camera/image_raw.
  • Now you can see the current simulated robot's view in the Camera window.

image-20250905165626951

5. Radar Data Visualization

Click "Add" in the Displays window on the left, select Laserscan, and then configure the subscription topic name. You should now see the laser dot.

image-20231031104528033

  • Select /sacn in the LaserScan topic.
  • Now you can see the LiDAR point cloud outline.

image-20250905170346004

6. Robot Model Visualization

Click "Add" in the Displays window on the left and select RobotModel.

image-20250905170711426

  • In the robot's DescriptionTopic, select /robot_deseription.
  • Now you can see the robot model visualized in Rviz2.

image-20250905170923074

7. Other Data Visualizations

The rivz_default_plugins section lists many commonly used data visualization plugins. Feel free to try them out.

image-20231031104758824

ROS2 common command tools
ROS2 rqt toolbox

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