MATLAB Implementation for Robot Localization Using Monte Carlo Algorithms

This article presents MATLAB code implementations for robot localization, with special emphasis on Monte Carlo localization algorithms. The Monte Carlo method is a numerical computation technique that employs random sampling to simulate real-world environments, enabling more accurate outcome predictions. In robot localization contexts, this typically involves particle filter implementations where each particle represents a possible robot state (position and orientation). Key algorithm components include: - Particle initialization and propagation based on motion models - Importance weighting through sensor measurement likelihood calculations - Resampling techniques to maintain particle diversity - Probability distribution estimation for position tracking Beyond robot localization, Monte Carlo methods are extensively used for probability distribution estimation and solving complex computational problems. The original implementation comes from TU Dortmund University, providing practical insights into both theoretical foundations and MATLAB coding practices for robotic localization systems. This resource aims to enhance understanding of Monte Carlo applications in autonomous navigation and sensor data processing.