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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

02.Camera calibration (USB)

Camera Calibration (USB)

Camera Calibration (USB)1. Install Plugins2. Start the Camera3. Image Calibration3.1. Calibration Board3.2. Viewing Topics and Services3.3. Image Calibration

1. Install Plugins

Install Image Calibration Tool

sudo apt install ros-${ROS_DISTRO}-image-pipeline -y

image-20260312162132002

Install Official USB Camera Tool

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sudo apt install ros-${ROS_DISTRO}-usb-cam -y

image-20260313114615124

2. Start the Camera

Restart the terminal, re-enable the ROS environment, and then run the following command to start the USB camera node.

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ros2 run usb_cam usb_cam_node_exe

image-20260313121210075

Open another terminal and launch rqt:

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rqt

image-20260313115135662

 

3. Image Calibration

3.1. Calibration Board

image-20250108190030306

 

3.2. Viewing Topics and Services

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ros2 topic list
ros2 service list

image-20260313115242141

image-20260313115300497

3.3. Image Calibration

Launch the Image Calibration Tool

Launch the ROS 2 camera calibration tool, camera_calibration, to perform the camera calibration. Note: The camera must be started before running the calibration tool.

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

Parameter Explanation

--size: The number of internal corners on the chessboard pattern → 9 rows by 6 columns of internal corners.

--square: The side length of each square on the chessboard pattern → 0.02 meters

--ros-args: Pass ROS arguments

--remap: Topic remapping → /image_raw serves as the image input stream

image-20260313120230263

Calibration Process

image-20260313120053912

Parameter Descriptions

X: Move the chessboard left and right within the camera's field of view

Y: Move the chessboard up and down within the camera's field of view

Size: Move the chessboard forward and backward within the camera's field of view

Skew: Tilt and rotate the chessboard within the camera's field of view

When X, Y, Size, and Skew turn green, you can click CALIBRATE to compute the camera's intrinsic parameters. Note: The more images used for calibration, the longer the process will take, and there is a possibility that the program may freeze.

image-20250115161835628

image-20250115161925727

Once calibration is complete, click SAVE to save the results!

image-20250115161935159

Close the program after the results have been successfully saved! Calibration Results

Default save location and filename for the calibration results:

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/tmp/calibrationdata.tar.gz

Copy and extract the calibration results:

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sudo mv /tmp/calibrationdata.tar.gz ~
mkdir calibrationdata
tar -xzvf calibrationdata.tar.gz -C ~/calibrationdata

The extracted files include the calibration *.png, ost.yaml, and ost.txt files. We will primarily copy the data contained within the ost.yaml file into the camera_usb.yaml file.

ost.yaml

Replace the corresponding data in the camera_usb.yaml file with the values ​​for image_width, image_height, the data field within camera_matrix, the data field within projection_matrix, and the data field within distortion_coefficients.

xxxxxxxxxx
image_width: 1280
image_height: 800
camera_name: narrow_stereo
camera_matrix:
  rows: 3
  cols: 3
  data: [2126.24686,    0.     , 1045.90416,
            0.     , 2038.73134,  292.1827 ,
            0.     ,    0.     ,    1.     ]
distortion_model: plumb_bob
distortion_coefficients:
  rows: 1
  cols: 5
  data: [0.618718, -0.496057, -0.038096, 0.134905, 0.000000]
rectification_matrix:
  rows: 3
  cols: 3
  data: [1., 0., 0.,
         0., 1., 0.,
         0., 0., 1.]
projection_matrix:
  rows: 3
  cols: 4
  data: [2049.23772,    0.     , 1082.02717,    0.     ,
            0.     , 2153.52835,  282.33049,    0.     ,
            0.     ,    0.     ,    1.     ,    0.     ]

camera_usb.yaml

Modified File:

xxxxxxxxxx
%YAML:1.0
---
image_width: 1920
image_height: 1080
camera_name: camera
camera_matrix: !!opencv-matrix
  rows: 3
  cols: 3
  dt: d
  data: [2126.24686,    0.     , 1045.90416,
            0.     , 2038.73134,  292.1827 ,
            0.     ,    0.     ,    1.     ]
distortion_model: plumb_bob
distortion_coefficients: !!opencv-matrix
  rows: 1
  cols: 5
  dt: d
  data: [0.618718, -0.496057, -0.038096, 0.134905, 0.000000]
rectification_matrix:
  rows: 3
  cols: 3
  data: [1., 0., 0.,
         0., 1., 0.,
         0., 0., 1.]
projection_matrix:
  rows: 3
  cols: 4
  data: [2049.23772,    0.     , 1082.02717,    0.     ,
            0.     , 2153.52835,  282.33049,    0.     ,
            0.     ,    0.     ,    1.     ,    0.     ]

 

Camera preview (USB)
AR vision (USB)

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