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About

Fei Xie is a robotics and autonomous systems researcher whose work spans mobile robot navigation, simultaneous localization and mapping (SLAM), and intelligent path planning. With a growing body of highly cited work, Xie has established a distinctive research profile centered on making robots smarter, faster, and more reliable in complex real-world environments. Among Xie's most recognized contributions is the FPS algorithm (2022, 29 citations), which dramatically accelerates path planning by combining sparse visibility graphs with bidirectional breadth-first search — addressing critical computational bottlenecks in occupancy grid-based navigation. Complementing this, Xie's work on optimized visibility graph-based planning and gradient-first search strategies further demonstrates a sustained effort to improve planning efficiency without relying on prior global maps. In the SLAM domain, Xie has contributed ULG-SLAM, a novel unsupervised learning approach for robust indoor localization, and a dynamic-scene visual SLAM system enhanced by instance segmentation — tackling the persistent challenge of accurate robot pose estimation in changing environments. More recently, work on autonomous exploration via LiDAR/IMU fusion and digital twin-driven manufacturing systems reflects an expanding research vision bridging robotics with smart production. Collectively, Xie's publications have accumulated over 90 citations, marking a meaningful impact on the robotics research community.