Xinghua Liu

Xi'an University of Technology, Academy of Military Medical Sciences

Papers

Total Citations

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About

Xinghua Liu is a leading researcher in resilient multi-robot systems and robust visual SLAM, with a focus on enabling autonomous navigation in challenging, real-world environments. Their work addresses critical vulnerabilities in multi-robot coordination, most notably in their highly cited 2020 paper on "Anomaly Resilient Relative Pose Estimation for Multiple Nonholonomic Mobile Robot Systems" (15 citations). This research pioneers methods to maintain accurate relative positioning among robot teams even when sensors like stereo cameras or laser rangefinders deliver faulty or abnormal measurements—a fundamental challenge for field robotics. Complementing this, Liu's 2021 work on "Ground Enhanced RGB-D SLAM for Dynamic Environments" (2 citations) introduces a novel approach to simultaneous localization and mapping that leverages static ground features to achieve robust pose estimation in environments cluttered with moving objects. By moving beyond conventional point-based SLAM, this work significantly improves map reconstruction stability. Together, Liu's contributions advance the frontier of resilient autonomy, providing practical solutions for deploying robot teams in unpredictable settings—from disaster response to industrial automation—where sensor reliability and environmental dynamics pose constant threats.