Robot SLAM Demo Program Based on Extended Kalman Filter (EKF)

This demonstration program utilizes the Extended Kalman Filter (EKF) algorithm for Simultaneous Localization and Mapping (SLAM) in robotic systems. The implementation features sensor data fusion through EKF's prediction-update cycle, where motion models predict robot pose and measurement models correct estimates using landmark observations. Key functions include state vector management for robot pose and landmark positions, alongside covariance matrix handling for uncertainty quantification. For developers working on robot localization and mapping applications, this program provides practical reference code structure with modular design for easy customization. The widely-tested version offers robust EKF-SLAM implementation with clear documentation on Jacobian calculations for non-linear transformations. Beyond core SLAM functionality, ongoing improvements focus on computational optimization and real-time performance enhancement. We welcome user feedback and suggestions to further refine the program's capabilities, including potential extensions like multi-sensor integration or advanced data association techniques.