Xing Liu

Papers

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About

Xing Liu is a robotics and autonomous systems researcher whose work centers on the critical challenges of navigation and spatial awareness for intelligent machines. His most recognized contribution lies in the domain of Simultaneous Localization and Mapping (SLAM), where his 2020 paper on stereo visual inertial mapping algorithms for autonomous mobile robots has garnered 10 citations, reflecting growing interest in this foundational area. Liu's research tackles the practical integration of Visual Inertial Odometry (VIO) into real-world autonomous mobile robot (AMR) deployments, addressing environments such as automated warehouses, factory material transfer systems, and flexible assembly operations — settings where reliable, real-time localization is mission-critical. By combining stereo vision with inertial sensing, his work advances the robustness and accuracy of robot navigation in complex industrial environments. Liu's contributions are particularly relevant at a time when intelligent transportation and smart manufacturing are rapidly expanding, making his research both timely and applicable. For students and researchers exploring autonomous robotics, his work represents a strong foundation in sensor fusion, mobile robotics, and the ongoing evolution of intelligent, self-navigating systems.