📌 ORB-SLAM3

ORB-SLAM3 is a state-of-the-art real-time Visual SLAM (Simultaneous Localization and Mapping) system. It supports monocular, stereo, RGB-D cameras, and visual-inertial fusion, making it highly suitable for robotics, drones, and autonomous systems.

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🔍 Features

• Real-time tracking

• Map creation and optimization

• Loop closure detection

• Support for multiple sensor setups

  • Monocular
  • Stereo
  • RGB-D
  • Visual-Inertial (IMU + Camera)

• Relocalization

• Works indoors and outdoors

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📦 Requirements

• Ubuntu 18.04 / 20.04
• C++17
• OpenCV
• Eigen3
• Pangolin
• Boost
• ROS (optional for ROS integration)

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🎥 Demo Video

Click the image below to watch the demo:

⚙️ Installation

1. Install dependencies

sudo apt-get update
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install libeigen3-dev libboost-all-dev
sudo apt-get install libopencv-dev

2. Clone the repository

git clone https://github.com/UZ-SLAMLab/ORB_SLAM3.git
cd ORB_SLAM3

3. Build

chmod +x build.sh
./build.sh

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🚀 Usage

Monocular

./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml PATH_TO_SEQUENCE

Stereo

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE

RGB-D

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM1.yaml PATH_TO_SEQUENCE

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🛰️ Visual-Inertial (IMU) Mode

./Examples/IMU/mono_inertial Vocabulary/ORBvoc.txt Examples/IMU/EuRoC.yaml PATH_TO_SEQUENCE

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📁 Folder Structure

ORB_SLAM3/
├── Examples/
├── ORB_SLAM3/
├── Thirdparty/
├── Vocabulary/
├── build/
└── README.md

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🧠 How it works (Short Overview)

1. Extract ORB features from images
2. Track keypoints across frames
3. Estimate camera pose
4. Build 3D map
5. Detect loop closure
6. Optimize map using bundle adjustment