Kalman Filter and EKF Implementation

This repository contains implementations of various Kalman Filter algorithms, including a basic Kalman Filter, Extended Kalman Filter (EKF), and localization examples. The code is written in Python and includes detailed explanations and visualizations.

Repository Structure

.
├── EKF-From-Scratch/       # Extended Kalman Filter implementation
├── KalmanFilter/           # Basic Kalman Filter implementation
├── Localization/           # Robot localization examples
└── Notes/                  # Theoretical explanations and mathematical foundations

Prerequisites

• Python 3.11 or higher
• pip package manager

Installation

1. Clone the repository:

git clone https://github.com/IremOztimur/AI4Robotics.git
cd AI4Robotics

2. Create and activate a virtual environment:

cd EKF-From-Scratch
make virtual # or make install to only install the dependencies

Usage

Extended Kalman Filter (EKF) SLAM

The EKF SLAM implementation demonstrates simultaneous localization and mapping:

cd EKF-From-Scratch
python ekf_main.py

Controls:

• Arrow keys to move the robot
• ESC or Q to quit

Basic Kalman Filter

Run the 1D Kalman Filter example:

cd KalmanFilter
python kalmanFilter1D.py

Localization

Run the robot localization example:

cd Localization
python localization.py

Testing

Run the test suite:

cd Localization
python test.py

Documentation

• Notes/Covariance.md - Detailed explanation of covariance matrices and their role in Kalman Filters
• Notes/Kinematics.md - Overview of kinematic equations used in motion models
• EKF-From-Scratch/README.md - Extended Kalman Filter theory and implementation details

Cleaning Up

Remove Python cache files:

make clean

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Acknowledgments

• Based on the AI for Robotics course materials
• Pygame library for visualization
• NumPy and SciPy for mathematical operations