Papers
6
Total Citations
89
H-Index
3
About
Xiaolin Meng is a researcher whose work sits at the intersection of structural health monitoring, positioning technologies, and intelligent sensing systems. Best known for pioneering applications of high-precision geospatial tools in civil infrastructure assessment, Meng has made significant contributions to understanding how bridges and rail networks respond dynamically to real-world loading conditions. His 2017 study experimentally validating high sampling-rate robotic total stations for monitoring bridge dynamic responses stands as his most influential work, accumulating 58 citations and establishing a methodological benchmark in the field. Complementing this, his collaborative research combining GPS and robotic total station measurements for pedestrian bridge monitoring addressed longstanding limitations of single-sensor approaches, while his 2022 investigation into low-cost multi-GNSS receivers demonstrated practical pathways toward affordable structural monitoring solutions. Beyond infrastructure, Meng has expanded his research portfolio into intelligent perception systems, including an end-to-end deep learning framework for visual camera relocalization, reflecting a broader interest in autonomous navigation and robotics. His railway monitoring contributions through projects like RailSat further underscore a commitment to translating advanced positioning science into operational, real-world applications across transportation networks.
Research Focus
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Top Papers
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