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RDK X3 Robot📖
01. About RDK-X3-Robot
1. Instructions
  • 1. Introduction
  • 2. FAQ
  • 3. Battery and charger use precautions
  • 4.TF card expand capacity
2. Common problem analysis
02. Assembly video
  • Part1-Install Motor
  • Part2-Install Battert Fixing Column
  • Part3-Install Motor Coupling
  • Part4-Install Mecanum wheel
  • Part5-Install Expansion Board
  • Part6-Wiring Show
  • Part7-Install Main Control Board
  • Part8-Install Car Top Plate and Antenna
  • Part9-Install OLED Screen
  • Part10-Install Depth Camera(Optional)
  • Part11-Install OLED Screen
  • Part12-Install HD Camera
  • Part13-Install Top Plate
  • Part14-Install Right Side Plate
  • Part15-Motor Wiring Show
  • Part16-lnstall Car Body
  • Part17-Install Battery
  • Part18-Install Lidar
03. Remote control tutorial
  • 1. The phone APP remote tutorial
  • 2. USB wireless controller remote tutorial
04. ROS functional tutorial
  • 1. Introduction of extension board
  • 2. Set up STM32 development environment
  • 3. Key control buzzer sound
  • 4. FreeRTOS application
  • 5. Serial communication
  • 6. SBUS model remote control
  • 7. CAN bus communication
  • 8. RGB color light strip
  • 9. Timer interrupt control PWM servo
  • 10. Control serial steering gear
  • 11. Nine-axis attitude sensor to obtain data
  • 12. Control motor forward and reverse
  • 13. The timer captures encoder data
  • 14. Theory of robot kinematics analysis
  • 15. PID control robot motion
05. Basic control tutorial
  • 1. Update the firmware of the expansion module
  • 2. Install the Python driver library
  • 3. Set the type of bottom car
  • 4. Open and close the APP program control
  • 5. Turn hotspot mode on and off
  • 6. The buzzer blows
  • 7. Control the motor forward and reverse
  • 8. Control robot movement
  • 9. OLED display
  • 10. Control fan and lamp effect
06. Development environment setup
  • 1. Virtual Machine
  • 2. Linux Basics
  • 3. Remote connection
  • 4. Multi-computer communication configuration
  • 5. Bind the device ID
  • 6. Image installation and backup
07. 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
08. Robot chassis control
  • 1. PID algorithm theory
  • 2. Robot information release
  • 3. Robot control
  • 4. Robot state estimation
  • 5. Robot calibration
  • 6. Robot trajectory tracking
  • 7. URDF model
09. Lidar courses
  • 1. Introduction and use of lidar
  • 2. Lidar obstacle avoidance
  • 3. Lidar following
  • 4. Lidar guard
  • 5. Patrol
  • 6. gmapping algorithm
  • 7. cartographer algorithm
  • 8. Navigation and obstacle avoidance
  • 9. App mapping and navigation
10. OpenCV courses
  • 1. Introduction to OpenCV
  • 2. OpenCV geometric transformation
  • 3. OpenCV image processing
  • 4. OpenCV image beautification
  • 5. Qr code creation and identification
11. MIPI camera course
  • 1. Camera driver
  • 2. Adjust the color HSV value
  • 3. Color recognition
  • 4. Color tracking
  • 5. Color follow
  • 6. Qr code command
  • 7. Face detection
  • 8. Object detection
  • 9. Visual Inspection -OpenCV
  • 10. Visual inspection - Deep learning
  • 11. Human tracking
  • 12. Gesture control
12. Depth Camera courses
  • 1. Use depth camera
  • 2. Calibration of camera internal reference
  • 3. ROS+OpenCV Foundation
  • 4. AR vision
  • 5. Color tracking
  • 6. Object tracking
13. Multi vehicle control
  • 1. Multi-vehicle synchronous control
  • 2. Multi-vehicle navigation
Remote login control
  • 1. VNC and SSH remote login
  • 2. Jupyter lab login and case code running
Download
  • RemoteCtrl_APP(iOS/Android))
  • Mapping_APP(iOS_Android)
  • Code
  • System File
  • VM_File
  • Manual
  • Software_File
  • Hardware_File
  • ROS2_Basic_VM
Download ZIP

Welcome to RDK X3 Robot repository

2. Calibration of camera internal reference

2. Calibration of camera internal reference

2. Calibration of camera internal reference2.1、Code Function Description2.2、Preparation before calibration2.3、Start up2.3.1、Start the camera2.3.2、Color Image Calibration2.3.3、ir infrared calibration

2.1、Code Function Description

Wiki:http://wiki.ros.org/camera_calibration

2.2、Preparation before calibration

  • This tutorial uses a 9x6 checkerboard and a 20mm square, flattened when scaling. Calibration uses the interior vertices of the checkerboard, so a "10x7" checkerboard uses interior vertex parameters of "9x6", as shown below.
  • An open area, free of obstacles and calibration board patterns.
  • Monocular camera that publishes images via ROS.

Checkerboard (calibration board)

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2.3、Start up

2.3.1、Start the camera

Take starting the Astra pro camera as an example, after SSH connects to the car, enter in the terminal,

ros2 launch astra_camera astro_pro_plus.launch.xml

Start the camera before calibration, and turn off the camera until all calibrations are completed.

Obi Zhongguang camera model and the corresponding launch file:

launch filecamera model
ros2 launch astra_camera astra_pro.launch.xmlAstrapro
ros2 launch astra_camera astro_pro_plus.launch.xmlAstraproplus
ros2 launch astra_camera astra.launch.xmlAstramini

Since the car has no graphical interface, the next calibration program needs to be run in a virtual machine.

Enter following command in the virtual machine terminal.

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ros2 topic list

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The topic we need to calibrate the RGB color image is /camera/color/image_raw, and the topic used to calibrate the ir infrared image is /camera/ir/image_raw.

2.3.2、Color Image Calibration

The virtual machine installs the camera calibration function package.

Input following command in virtual machine terminal.

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sudo apt install ros-foxy-camera-calibration*

Input " y", press Enter to start the installation, and wait for the installation to complete.

Input following command in virtual machine terminal.

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ros2 run camera_calibration cameracalibrator --size 9x6 --square 0.02 --ros-args --remap /image:=/camera/color/image_raw
  • size: Calibrate the number of internal corners of the checkerboard, for example, 9X6, the corners have a total of six rows and nine columns.
  • square: The side length of the checkerboard, in meters.
  • /camera/color/image_raw: RGB image topic name.

1684909551282

  • X: left and right movement of the checkerboard in the camera field of view
  • Y: The up and down movement of the checkerboard in the camera field of view
  • Size: the checkerboard moves back and forth in the camera field of view
  • Skew: The tilt and rotation of the checkerboard in the camera field of view

Collect images by moving up, down, back and forth, left and right, and turning them over, so that the [X], [Y], [Size], and [Skew] on the right turn green, as shown in the figure below, and then click [CALIBRATE] to start calculating the internal camera parameters. About 60-70 pictures.

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After the calibration is completed, click [SAVE], as shown in the figure below.

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The calibration result is saved to [/tmp/calibrationdata.tar.gz], and the file can be moved out to view the content.

After exiting the program, enter the following command in the virtual machine terminal.

x
#move the file to the current directory
sudo mv /tmp/calibrationdata.tar.gz .
#create folder
mkdir calibrationdata_rgb
#Unzip to a specified folder
tar -zxvf calibrationdata.tar.gz -C ./calibrationdata_rgb

1684911685467

We will get the calibrated png file, ost.yaml and ost.txt files in the calibrationdata_rgb folder in this directory.

Since the Astra driver loads the calibrated built-in parameters in the code when it starts, there is no need to load the calibrated parameters. In the case of [AR vision] in [opencv series courses], the internal reference of the camera will be used.

2.3.3、ir infrared calibration

Since the RGBD camera uses structured light as the depth imaging principle, the infrared light projected by it is a special disordered spot, which makes the infrared receiving device unable to receive clear and complete picture content.

At this point we can have several special processing methods:

  • Forcibly find various angles, let the camera find corners as much as possible (poor accuracy)
  • By pasting some frosted translucent paper in front of the red hair emitter, even out the infrared spot (moderate accuracy, more convenient)
  • Block the infrared projection camera and use an external infrared camera to fill in the light (high precision, requires additional equipment)

Choose a processing method according to your needs.

Enter the following command in the virtual machine terminal.

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ros2 run camera_calibration cameracalibrator --size 9x6 --square 0.02 --ros-args --remap /image:=/camera/ir/image_raw

1684910374142

In the same way, the following operation is similar to the color camera calibration, change different poses, and fill the lines under [X], [Y], [Size], and [Skew] as much as possible.

1684910952649

Click [CALIBRATE] to calculate the internal parameters of the camera, and click [SAVE], as shown below.

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The calibration result is saved to [/tmp/calibrationdata.tar.gz], and the file can be moved out to view the content.

After exiting the program, enter the following command in the virtual machine terminal.

x
#move the file to the current directory
sudo mv /tmp/calibrationdata.tar.gz .
#create folder
mkdir calibrationdata_depth
#Unzip to a specified folder
tar -zxvf calibrationdata.tar.gz -C ./calibrationdata_depth

1684911041314

We will get the calibrated png file, ost.yaml and ost.txt files in the calibrationdata_depth folder in this directory.

 

Use depth camera
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