Updated Implementation of Extended Kalman Filter for SLAM

This documentation introduces the latest version of our codebase implementing Simultaneous Localization and Mapping (SLAM) using the Extended Kalman Filter (EKF). The code is engineered for robotic positioning and automated map generation. Notably, the Extended Kalman Filter serves as a prevalent filtering technique for nonlinear state estimation in measurement systems. Our implementation employs Jacobian matrices to linearize nonlinear motion and observation models at each estimation step, enabling more precise robot pose tracking. The algorithm processes sensor data through prediction and correction phases - using motion models for state prediction and sensor measurements for state updates with Kalman gain optimization. This approach significantly enhances robotic navigation and positioning accuracy across diverse environments. Furthermore, our optimized code structure accelerates automated map construction speed and precision through efficient covariance propagation and landmark management, substantially reducing manual mapping efforts while improving operational efficiency. We highly recommend this EKF-SLAM implementation as the primary solution for robotic localization and automated mapping tasks.