Visual Odometry Implementation

Research-ready Visual Odometry implementation for robotics education and research.

DISCLAIMER

WARNING: This software is for educational and research purposes only. DO NOT use on real robots without proper safety measures, expert review, and extensive testing. This implementation lacks safety-critical features required for real-world deployment.

Features

• Multiple Visual Odometry algorithms (ORB, SIFT, Optical Flow)
• Comprehensive evaluation metrics (ATE, RPE, drift analysis)
• ROS 2 integration with proper message types
• Interactive demos and visualizations
• Simulation data generation
• Modern Python practices with type hints and documentation

Quick Start

Installation

# Clone the repository
git clone https://github.com/kryptologyst/Visual-Odometry-Implementation.git
cd Visual-Odometry-Implementation

# Install dependencies
pip install -e .

# Install optional dependencies
pip install -e ".[ros2,simulation,learning]"

Basic Usage

# Run the basic visual odometry demo
python scripts/run_vo_demo.py

# Run with ROS 2 (if installed)
ros2 launch visual_odometry vo_demo.launch.py

# Run interactive demo
streamlit run demo/streamlit_app.py

Project Structure

├── src/                    # Source code
│   ├── visual_odometry/    # Core VO algorithms
│   └── utils/              # Utility functions
├── robots/                 # Robot descriptions
│   ├── urdf/              # URDF files
│   └── meshes/            # 3D meshes
├── launch/                # ROS 2 launch files
├── config/                # Configuration files
├── data/                  # Datasets and sample data
├── scripts/               # Command-line scripts
├── notebooks/             # Jupyter notebooks
├── tests/                 # Unit tests
├── assets/                # Generated artifacts
└── demo/                  # Interactive demos

Algorithms

• ORB-based VO: Oriented FAST and Rotated BRIEF features
• SIFT-based VO: Scale-Invariant Feature Transform
• Optical Flow: Lucas-Kanade and Farneback methods
• Essential Matrix: RANSAC-based outlier rejection

Evaluation Metrics

• ATE (Absolute Trajectory Error): Overall trajectory accuracy
• RPE (Relative Pose Error): Frame-to-frame accuracy
• Drift Analysis: Long-term trajectory drift
• Feature Tracking: Match quality and consistency

Safety and Limitations

• Simulation-only implementation
• No real-time guarantees
• Limited to indoor environments
• Requires good lighting conditions
• No obstacle avoidance
• No collision detection

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Visual-Odometry-Implementation