MATLAB Implementation for Robot Navigation

This document presents a robot navigation program implemented using particle filter and extended Kalman filter techniques. The robot navigation program is a software solution that enables autonomous robots to navigate through unknown environments. Particle filter is a Bayesian filtering-based technique primarily employed for nonlinear state estimation and non-Gaussian distribution filtering problems, typically implemented through sequential Monte Carlo methods with resampling procedures. Extended Kalman filter is derived from the standard Kalman filter and optimized for state estimation in nonlinear systems, utilizing first-order Taylor series expansion for linearization around the current mean and covariance. These advanced algorithms collectively form a sophisticated navigation system that empowers robots to perform reliable navigation in complex operational environments. The implementation includes key functions for probability distribution modeling, sensor data fusion, and pose estimation updates.