Welcome to the MultiEmbedded Blog, where we share tutorials, reviews, and comparisons on embedded systems, AI hardware, and robotics platforms including Arduino, Raspberry Pi, and NVIDIA Jetson.

Post Content (copy everything below)

Introduction

The NVIDIA Jetson Orin Nano is a powerful platform for edge AI, robotics, and computer vision projects. In this tutorial, I’ll walk through how I configured my Jetson Orin Nano from a fresh install to a fully working AI development environment, including JetPack setup, CUDA, TensorRT, camera support, and Python tools.

This guide is practical and based on my real configuration process.

Hardware Used

NVIDIA Jetson Orin Nano Developer Kit
256GB NVMe SSD
CSI Camera (HBV-Raspberry-150FPC)
19V Power Adapter
HDMI monitor, keyboard, mouse
Ethernet connection

Step 1 – Flashing JetPack OS

I used NVIDIA SDK Manager on Ubuntu to flash JetPack.

Steps:

Install SDK Manager on PC: sudo apt install sdkmanager
Put Jetson into recovery mode:
- Hold Force Recovery
- Press Power
- Release after 3 seconds
Open SDK Manager
Select:
- Jetson Orin Nano
- JetPack 6.x
Flash to NVMe SSD

Flashing took about 15 minutes.

Step 2 – First Boot Configuration

After flashing:

Set username and password
Configure timezone
Enable WiFi / Ethernet
Update system:

sudo apt update && sudo apt upgrade -y

Step 3 – Verify CUDA & GPU

To confirm CUDA:

nvcc --version

To check GPU:

nvidia-smi

Step 4 – Install Python AI Environment

sudo apt install python3-pip python3-venv -y

Create virtual environment:

python3 -m venv ai_env
source ai_env/bin/activate

Install libraries:

pip install numpy opencv-python torch torchvision ultralytics

Step 5 – Install TensorRT

JetPack includes TensorRT, verify with:

dpkg -l | grep tensorrt

Step 6 – Camera Setup (CSI)

List cameras:

v4l2-ctl --list-devices

Test camera:

gst-launch-1.0 nvarguscamerasrc ! nvvidconv ! xvimagesink

Step 7 – YOLOv8 Setup

pip install ultralytics

Test:

yolo predict model=yolov8n.pt source=0

Step 8 – Performance Optimization

Enabled:

MAXN mode

sudo nvpmodel -m 0
sudo jetson_clocks

Increased swap:

sudo fallocate -l 8G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

Step 9 – ROS2 Installation (Optional)

sudo apt install ros-humble-desktop

Final Setup Summary

My Jetson Orin Nano is now configured for:

Real-time AI inference
YOLOv8 object detection
CSI camera streaming
ROS2 robotics projects
CUDA + TensorRT acceleration

Common Problems I Faced

Issue	Solution
Camera not detected	Reboot + reseat cable
CUDA not found	Reinstall JetPack
Low FPS	Enable MAXN mode
Out of memory	Add swap file

Conclusion

The Jetson Orin Nano is an excellent platform for edge AI development when configured correctly. With proper setup, it can run advanced computer vision pipelines efficiently and reliably.

If you’re building robotics, smart cameras, or AI-powered systems, this board is a strong choice.

Categories