Simultaneous Localization and Mapping with Compressed Extended Kalman Filter Algorithm

In this project, we implement a simultaneous localization and mapping (SLAM) algorithm that employs compressed extended Kalman filter (CEKF) technology to enhance estimation accuracy. The implementation involves designing custom robot trajectories and landmark configurations for experimental validation. The algorithm utilizes sensor measurement models and state transition functions to recursively update the robot's pose estimation and environmental map. Key components include: - State vector compression techniques to reduce computational complexity - Jacobian matrix calculations for linearization in the EKF framework - Landmark initialization and data association methods - Covariance matrix management for uncertainty propagation The localization process is visualized through dynamic animations showing the robot's estimated path, landmark observations, and uncertainty ellipses. This algorithm has significant applications in robotics and autonomous vehicle navigation systems. Through this implementation, we aim to contribute to the field while deepening our understanding of SLAM algorithms and their practical implementation challenges.