Zongwen Xue

Papers

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About

Zongwen Xue is a robotics and autonomous systems researcher whose work centers on state estimation, sensor fusion, and nonlinear filtering techniques for mobile robot navigation. His research has made meaningful contributions to the field of mobile robot pose estimation — the challenge of accurately determining a robot's position and orientation in the presence of real-world noise and uncertainty in nonlinear, non-Gaussian systems. Xue's most recognized contribution is his systematic comparative analysis of nonlinear filtering algorithms, specifically the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). His 2013 paper on this topic has garnered 13 citations and remains a valuable reference for researchers selecting appropriate filtering strategies for robotic localization tasks. He extended this work in 2015, grounding his comparisons in both simulation and experimental results, lending greater practical credibility to his findings. By rigorously evaluating filtering performance under realistic conditions, Xue has helped clarify the trade-offs between computational efficiency and estimation accuracy — a question of direct relevance to engineers designing real-world autonomous systems. His body of work serves as a useful foundational resource for students and practitioners navigating the increasingly important domain of robot state estimation and autonomous navigation.