MATLAB Implementation of SLAM Algorithm

This document presents a SLAM (Simultaneous Localization and Mapping) algorithm implementation developed by the University of Sydney. The program's primary functionality focuses on realizing core SLAM algorithm principles, where robots or mobile devices can perform autonomous navigation and positioning in unknown environments. The implementation typically involves key components such as sensor data processing, pose estimation, landmark extraction, and map building through probabilistic frameworks like Extended Kalman Filters (EKF) or particle filters. This technology has numerous practical applications, including autonomous vehicles, robotic navigation systems, and indoor positioning within building structures. The code implementation provides essential support for these applications by offering modular functions for data association, state prediction, and covariance management, enabling researchers and developers to build upon the foundational SLAM architecture for various real-world scenarios.