Xinyi Li

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About

Xinyi Li is a robotics researcher whose work bridges the gap between biological navigation and autonomous systems. Her primary research focuses on LiDAR-based global localization for mobile robots, drawing inspiration from animal instinct to solve the persistent "kidnapped robot problem" in indoor environments. Li’s most impactful contribution is her 2023 paper on "Fast and deterministic (3+1)DOF point set registration with gravity prior," which has already garnered 19 citations. This work introduces a novel, deterministic approach to aligning 3D point clouds by leveraging gravity as a prior constraint, dramatically improving speed and reliability over traditional probabilistic methods. In her earlier 2022 study, "A Biologically-Inspired Global Localization System for Mobile Robots Using LiDAR Sensor," Li demonstrated how animals’ innate ability to orient themselves can be translated into algorithmic frameworks, achieving robust localization without prior pose estimates. Her research is particularly notable for addressing the computational inefficiencies that have historically plagued probabilistic localization in feature-sparse indoor environments. By combining biological principles with deterministic geometry, Li is advancing the frontier of reliable, real-time robot navigation—a critical capability for service robots, autonomous warehouses, and assistive technologies.