Papers
7
Total Citations
234
H-Index
7
About
Xiyuan Chen is a prominent researcher specializing in indoor robot localization, sensor fusion, and state estimation, with particular expertise in designing advanced filtering algorithms for navigation systems. His work addresses one of robotics' most persistent challenges: achieving accurate, robust positioning in GPS-denied indoor environments. Chen's major contributions center on developing and refining sophisticated filtering frameworks — including Extended Kalman Filters (EKF), Finite Impulse Response (FIR) filters, and their hybrid variants — integrated with diverse sensing technologies such as Ultra-Wideband (UWB), LiDAR, ultrasonic systems, and Inertial Navigation Systems (INS). His 2018 work combining EKF with Extended Unbiased FIR filtering for UWB-based localization and his 2023 study tackling colored measurement noise in UWB systems each garnered around 47–51 citations, reflecting sustained community interest. His 2019 cascaded FIR filter approach for INS/LiDAR integration further demonstrated his commitment to improving real-world robustness. Across his career, Chen has consistently pushed beyond conventional filtering limitations, developing adaptive and iterative estimation methods that handle real-world noise and uncertainty. With multiple papers exceeding 45 citations, his cumulative influence on indoor navigation research makes him an essential reference for engineers and researchers building next-generation autonomous robotic systems.
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