SLAM with MATLAB

This toolbox serves as an excellent resource for learning SLAM implementation with MATLAB. It provides a comprehensive suite of functions and utilities for handling key SLAM components including map management, sensor data processing, and pose estimation algorithms. The toolbox enables users to implement SLAM algorithms using MATLAB's built-in functions for matrix operations (like matrix inversion and eigenvalue decomposition), sensor fusion techniques, and optimization methods for pose graph optimization. Key features include implementations of EKF-SLAM (Extended Kalman Filter SLAM) and FastSLAM algorithms with modular code structure, allowing users to modify individual components like motion models, observation models, and data association logic. The toolbox contains practical examples demonstrating how to process LiDAR point clouds using MATLAB's pointCloud object, implement scan matching with ICP algorithms, and manage landmark initialization using geometric constraints. Additionally, the package includes extensive tutorials covering fundamental SLAM theory and practical implementation techniques, featuring step-by-step code walkthroughs for setting up simulation environments, configuring sensor parameters, and evaluating SLAM performance metrics. These resources help users quickly master both theoretical concepts and hands-on implementation skills while improving algorithm efficiency and reliability through optimized MATLAB coding practices.