Year: 2025
Type: conference-paper
Abstract: 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.
Author: Rishabh Changwani
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414405461