Optimized State Estimation for Real-Time Robotic Navigation and Mapping

This study explores an advanced state estimation framework for robotic localization and mapping using a probabilistic filtering approach. By leveraging an Extended Kalman Filter (EKF), the system continuously refines position estimates through sensor fusion and iterative corrections. The proposed method enhances real-time trajectory tracking and environmental mapping, addressing challenges in uncertainty propagation and computational efficiency. Experimental validation demonstrates improved accuracy in dynamic navigation scenarios, providing a scalable solution for autonomous robotic systems operating in uncertain environments.