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SLAN: Simultaneous Localization, Mapping and Navigation

This project implements a complete SLAN framework for autonomous mobile robots using Python and CoppeliaSim. It integrates real-time LiDAR-based mapping, EKF localization, frontier-based exploration, A* path planning, and a simple loop closure mechanism.

Features

  • Occupancy grid mapping using log-odds
  • EKF-based pose estimation
  • Frontier-based goal selection
  • A* path planning
  • Real-time visualization
  • Loop closure correction via pose history comparison

Requirements

See requirements.txt for dependencies.

Usage

# Clone the repository
https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
cd YOUR_REPO_NAME

# Install dependencies
pip install -r requirements.txt

# Run the main SLAN simulation
python main.py

File Structure

├── main.py                # Main loop for SLAM and navigation
├── occupancy_grid.py      # Mapping logic
├── localization.py        # EKF localization with loop closure
├── mapping.py             # Frontier detection and A* path planning
├── controller.py          # Motion control logic
├── sensors.py             # LiDAR data handling
├── bresenham.py           # Ray tracing algorithm for grid updates
├── requirements.txt       # Python package dependencies
├── README.md              # Project overview and instructions

Authors

  • Sadiq Abubakar
  • Mohib Riyaz

Supervisor

  • Professor Andrea Cherubini

About

This project extends the classical SLAM framework by integrating real-time navigation and control, resulting in a full SLAN (Simultaneous Localization, Mapping and Navigation) system.

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