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Jetson AGX Orin📖
01.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flash new system to the onboard eMMC
  • 03.Flash new system to an NVMe SSD
  • 04.Install Jetpack environment
  • 05.Backup SSD system
  • 06.Restore factory image system
  • 07.SSD expansion
  • 08.Solid State Drive Speed Test
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
  • 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
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.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
06.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
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
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
06.YOLO26 development
  • 01.YOLO Environment Setupnew
  • 02.CLI Usagenew
  • 03.Object Detectionnew
  • 04.Instance Segmentationnew
  • 05.Pose Estimationnew
  • 06.Image Classificationnew
  • 07.Oriented Bounding Boxes 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.ROS2-Humble
01.ROS2 basic course
  • 3.ROS2 development environment
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 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
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
10.Offline AI model course
Offline large model (Text Interaction)
  • 10.AI large model offline voice assistant
  • 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
11.Online AI model course
Online large model (Text Interaction)
  • 8.AI large model offline voice assistant
  • 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
12.Offline Generative AI Applications
  • 01.Children's Storytelling Session
  • 02.Cooking Assistant
  • 03.Homework tutor
  • 04.Image to animation
13.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
Download
  • System_File
  • VM_File
  • Code
  • Tools
Download ZIP

Welcome to Jetson AGX Orin 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。

02.Camera calibration (USB)

Camera calibration (USB)

Camera calibration (USB)1. Start the camera2. Image calibration2.1. Calibration board2.2. View topics2.3. Image calibration2.3.1. Install the image calibration tool2.3.2. Start the image calibration tool2.3.3 Calibration process2.3.4, Calibration results

1. Start the camera

cd ~/yahboom_ws
xxxxxxxxxx
source install/setup.bash
xxxxxxxxxx
ros2 run camera camera_usb

image-20250115161158222

2. Image calibration

2.1. Calibration board

image-20250108190030306

2.2. View topics

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ros2 topic list

image-20250115161238107

2.3. Image calibration

2.3.1. Install the image calibration tool

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sudo apt update
sudo apt install ros-humble-image-pipeline -y

image-20250115093941501

2.3.2. Start the image calibration tool

Start the ROS2 camera calibration tool camera_calibration and perform camera calibration: Before calibration, you need to start the camera first

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

Parameter Description

--size:The number of inner corner points of the chessboard → 9 rows and 6 columns of inner corner points

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

--ros-args:Passing ROS parameters

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

image-20250115161718362

2.3.3 Calibration process

image-20250115161740573

Parameter Description

X:The chessboard moves left and right in the camera's field of view

Y:The chessboard moves up and down in the camera's field of view

Size:The chessboard moves back and forth in the camera's field of view

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

When X, Y, Size, and Skew turn green, you can click CALIBRATE to calculate the camera internal parameters: The more pictures you calibrate, the longer it takes, and the program may get stuck.

image-20250115161835628

image-20250115161925727

After calibration, click SAVE to save the results!

image-20250115161935159

Close the program after successful saving!

2.3.4, Calibration results

Default save location and file name of calibration results:

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/tmp/calibrationdata.tar.gz

Copy and decompress the calibration results:

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

The decompressed files contain the calibration *.png, ost.yaml and ost.txt files. We mainly copy the data in the ost.yaml file to the camera_usb.yaml file.

ost.yaml

Replace the camera_usb.yaml file data with the image_width, image_height, data in camera_matrix, data in projection_matrix, and data in distortion_coefficients.

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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_csi.yaml

Modified file:

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%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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