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Raspberry Pi 5📖
00.Quick Start (Must See)
  • 1.Quick Start (Must See)
  • 2.TF card expand capacity
  • 3.Raspberry Pi 5 - 1GB Version Must - See Before Use
01.Introduction to Raspberry PI 5
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Installation and introduction of shell and active radiator
  • 6. GPIO and 40-pin pins
  • 7. Tips for using Raspberry PI 5
  • 8. Raspberry PI PCIe connector
  • 9. MIPI CSI DSI connector
  • 10. UART connector
  • 11. Real-time clock
  • 12. Power over Ethernet (PoE) connector
02.Introduction to common mirror systems
  • 1. Official Raspberry PI system
  • 2. Ubuntu
03.Basic settings of Raspberry Pi
  • 1. Update and upgrade operating system
  • 2.Introduction to raspi-config tool
  • 3.config.txt file description
  • 4.Network Configuration
  • 5.remote access
  • 6.Transfer files remotely
  • 7.Set display resolution and rotation
  • 8.Set screen to sleep
  • 9.Get real-time temperature of Raspberry Pi
  • 10.Play audio and video
  • 11.Using USB camera
  • 12.Using MIPI camera
04.Basic programming course
1. C language programming
  • 1. C language environment construction
  • 2. Print Hello World
2. Python programming
  • 1.Python usage
  • 2.Use APT to install Python packages
  • 3.Python virtual environment
  • 4.GPIO Zero
  • 5.Pin output high and low levels
  • 6.Pin reads high and low levels
  • 7.Pin output PWM
3.Jupyter lab programming
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
05.Advanced AI course
5.Advanced AI gameplay
  • 1. Human pose estimation
  • 2. target detection
  • 3.yolov5
  • 4. Camera display
  • 5. QR code
  • 6. face detection
  • 7. Color recognition
2.Getting started with Open Source CV
  • 1. Introduction to Open Source CV
  • 2. Read and display Open Source CV images
  • 3. Open Source CV image writing
  • 4. Open Source CV image quality
  • 5.Open Source CV pixel operation
3.Open Source CV Geometric Transformations
  • 1. Image resizing
  • 2. Cut the image
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine transformation
  • 6. Rotate the image
  • 7. Perspective transformation
4.Text Picture Drawing Open Source CV image processing and drawing text line segments
  • 1、Grey Processing
  • 2、image binaryzation
  • 3、edge detection
  • 4、Line segment drawing
  • 5、Rectangle and circle drawing
  • 6、Text image drawing
1.Open Source CV environment setup
  • Open Source CV environment setup
6.YOLOv11 development
  • 1. YOLOv11 environment construction
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model conversion
  • 9. Dataset Annotation
  • 10. Model training_conversion
  • 11. Model prediction
7.YOLO26 development
  • 01.YOLO26 environment
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented bounding box object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training - conversion
  • 11.Model prediction
06.Mediapipe course
  • 1. Face special effects
  • 2. Face detection
  • 3. Three-dimensional object recognition
  • 4. Paintbrush
  • 5. Finger control
  • 6. Gesture recognition
07.Docker Tutorial
  • 1.Overview and installation
  • 2.Common commands
  • 3.Understand and publish images
  • 4.Hardware interaction data processing
  • 5.Enter docker container
  • 6.Update docker images
  • 7. docker container to install ROS using accessories
08.ROS1 advanced tutorial
1.ROS1 basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
2.ROS1 vision application
  • 0.Precautions
  • 1.AR vision
  • 2.AR QR code
  • 3.ROS+Opencv basics
  • 4.Face recognition
  • 5.harris corner detection
  • 6.Target tracking algorithm
  • 7.Contour moment
  • 8.Polygon outline
  • 9.Discrete fourier transform algorithm
  • 10.Edge detection algorithm
  • 11.Face detection algorithm
  • 12.Optical flow detection algorithm
  • 13.Contour detection
  • 14.General contour detection
  • 15.Feature point tracking
  • 16.HLS color filtering
  • 17.Hough circle detection
  • 18.Hough linear detection
  • 19.HSV color filtering
  • 20.LK optical flow algorithm
  • 21.detection algorithm
  • 22.Phase dependent displacement detection
  • 23.Image pyramid sampling algorithm
  • 24.RGB color filtering
  • 25.Clear background detection algorithm
  • 26.Simplified optical flow algorithm
  • 27.Simple filter
  • 28.Threshold image processing
  • 29.Watershed segmentation algorithm
09.ROS2 advanced tutorial
1.ROS2 basic course
  • 1. Introduction to ROS2
  • 2.ROS2 install Humble
  • 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 application
  • 0.Precautions
  • 1.Opencv basic course
  • 2.ROS+opencv application
  • 3.QR code creation and recognition
  • 4.AR vision
  • 5.Interesting gameplay of mediapipe
10.Offline AI model coursenew
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Switch Chinese input method
  • 3. Installation of large model dialogue platform
  • 4. Meta AI Llama 3.2 model
  • 5. Alibaba Cloud Qwen3 Model
  • 6. Microsoft Phi-4-mini model
  • 7. DeepSeek DeepSeek-R1 model
  • 8. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 9. Google Gemma3 Visual Multimodal Large Model
  • 10. Llava Visual Multimodal Large Model
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Visual Understanding Applications
  • 13. Multimodal Text-Based Image Application
  • 14. Multimodal Video Analysis Application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal table scanning application
  • 17. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
11.Online AI model coursenew
Online large model (text interaction)
  • 1.OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal Visual Understanding Applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (voice interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
12.OpenClaw Deployment and Basic Usagenew
  • 01. OpenClaw Deployment
  • 02. OpenClaw WhatsApp Plugin Application
  • 03. OpenClaw WebChat Interaction
  • 04. OpenClaw TUI Interaction
  • 05. OpenClaw Tools Introduction
  • 06. OpenClawGate Gateway User Manual
  • 07. OpenClaw Features Overview
  • 08. OpenClawHub Introduction (Skill Installation)
  • 09. OpenClaw Application - File Management
  • 10. OpenClaw Application - Camera
  • 11. OpenClaw Application - Browser Control
  • 12. OpenClaw Application - Script Execution
  • 13. OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 14. OpenClaw Application - Dedicated AI Assistant
13.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. 3D 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
  • SystemFile
  • Code
  • SoftWare
  • 3D_Model_File
  • GuideManual
Download ZIP

Welcome to Raspberry Pi 5 repository

16.ROS parameter service

16.ROS parameter service

The parameter server is where the node stores parameters. You can modify the parameters to achieve the function of debugging the program. In the ros system, the tool for parameter servers is rosparam. This lesson explains how to use the rosparam command.

image-20231025092917164

Among them, parameter represents the actual parameter, and file represents the actual file.

16.1 rosparam list

This command is used to list all the parameters in the parameter server of the currently running ros node. For example, we run the node of the little turtle, and then use this command to see what parameters there are and run the terminal.

roslaunch learn_launch turtle_node.launch

After running successfully, enter rosparam list in another terminal to query the parameters.

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rosparam list

image-20231025093437965

16.2 rosparam get [parameter]

This command is to get the parameter value of a certain parameter. You can query it by typing rosparam get on the terminal. Then use the little turtle program above to query the value of /turtle/background_b. Enter the terminal.

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rosparam get /turtle/background_b

image-20231025094105447

16.3 rosparam set [parameter] [value]

This command is to set the parameter value of a certain parameter. You can set the parameter value by typing rosparam set in the terminal. Then follow the little turtle program above and set /turtle/background_b to 0 to see the effect. Terminal input,

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rosparam set /turtle/background_b 0

It is found that the background of the little turtle has not changed after input. That is because this parameter does not support dynamic adjustment. We can use the above command rosparam get to obtain it again and enter it in the terminal.

xxxxxxxxxx
rosparam get /turtle/background_b

image-20231025095546843

As shown in the figure above, the query found that the parameter value of /turtle/background_b has been changed to 0.

16.4 rosparam delete [parameter]

This command is used to delete a certain parameter. Enter rosparam delete parameter to be deleted in the terminal to delete the parameter. Then use the little turtle program above to delete the /turtle/background_b parameter and enter in the terminal.

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rosparam delete /turtle/background_b

Then, enter rosparam list to check whether there is still /turtle/background_b after deletion.

xxxxxxxxxx
rosparam list

image-20231025101353141

The query revealed that the /turtle/background_b parameter no longer exists.

16.5 rosparam dump [file]

This command is used to export all parameter values of the current ros parameter service table and save them to a file. Enter rosparam dump save parameter file in the terminal to export to the specified file.Then use the little turtle program above to export the parameters to turtle.yaml in the terminal directory and input it into the terminal.

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rosparam dump turtle.yaml

In the terminal directory, you can see a turtle.yaml file. Double-click it to open it, as shown in the figure below.

image-20231025102051522

16.6 rosparam load [file]

This command can load the parameter table file to the parameter server to realize the function of modifying multiple parameters at the same time. Enter rosparam load parameter table file name in the terminal,We have just exported a turtle.yaml parameter table, copy and modify the contents, name it turtle_changed.yaml, and change the background_g and background_r values in it to 0 and 255.

​x
rosdistro: 'noetic

  '
roslaunch:
  uris:
    host_localhost__35295: http://localhost:35295/
rosversion: '1.16.0

  '
run_id: 7e98ba4a-72d6-11ee-b168-f15ddb9cdb6a
turtle:
  background_g: 0
  background_r: 255

Exit after saving, then open the terminal in the directory where the parameter table file is located and enter,

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rosparam load turtle_changed.yaml

Then, we enter the parameter query command to query the values of background_g and background_r,

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rosparam get /turtle/background_g
rosparam get /turtle/background_r

image-20231025103201903

From the query, we found that the values set in our parameter table are indeed consistent.

16.7 Launch file loading param

We mentioned the concept of param parameters in the course [14. ros-launch file]. In the launch file, you can also pass in this yaml to set parameters. Generally, we will create a new param folder under the launch folder to store the parameter table file, so we create a folder under the learn_launch folder and name it param.

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cd ~/ros_ws/src/learn_launch
mkdir param

Then, in the param folder, create a new file named background_rgb.yaml, which is used to set the background board of the little turtle. Copy the following content to background_rgb.yaml,

xxxxxxxxxx
background_b: 0
background_g: 100
background_r: 255

Pay attention to the colon: there is a space after it. The background_b, background_g, and background_r here correspond to the parameters of the little turtle. Save and exit. Create a new launch file in the launch folder and name it turtle_param_set.launch. Copy the following content into it. ,

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<launch>
    <node pkg="turtlesim" type="turtlesim_node" name="turtle">
        <rosparam file="$(find learn_launch)/param/background_rgb.yaml" command="load"/>
    </node>  
</launch>

Enter the following command in the terminal to run the launch file:

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roslaunch learn_launch turtle_param_set.launch

image-20231025112438988

After successful operation, as shown in the picture above, the terminal will also print out the values of background_b, background_g, and background_r, which are consistent with those in the parameter table, indicating that the loading is successful, and the background of the little turtle has also been changed from the default blue to the color shown in the picture . In the launch file, the code for loading the parameter table is as follows:

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<rosparam file="$(find pkg_name)/param/file_name.yaml" command="load"/>

Among them, pkg_name is the name of the function package, file_name.yaml represents the name of the parameter table file, and the default parameter table file is placed in the param folder under the function package folder. Combined with the cases, the corresponding result is,

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<rosparam file="$(find learn_launch)/param/background_rgb.yaml" command="load"/>

 

ROS-TF transformation
ROS-rviz use

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