Man Lin

Papers

Total Citations

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About

Man Lin’s research lies at the intersection of robotics, signal processing, and estimation theory, with a central focus on improving the accuracy and reliability of robot navigation under uncertainty. Her major contributions center on the development of advanced filtering techniques that address the challenge of noisy sensor data. Specifically, Lin pioneered the application of total least squares methods—including the Rayleigh Quotient Iteration filter (1998) and an iterative total least squares filter (2002)—to refine position estimation in robotic systems. These works offer a robust alternative to the traditional discrete Kalman filter, which, while widely used in communication and control, can be suboptimal when both the system model and measurements contain errors. By directly tackling errors-in-variables problems, Lin’s filters provide more accurate state estimates in real-world, noise-corrupted environments. Though her most-cited papers have accumulated modest citation counts (3 and 2, respectively), they represent foundational steps in a niche but critical area of sensor fusion. Her work is particularly notable for bridging theoretical least squares optimization with practical robotic navigation, offering a principled framework that continues to inform research on robust localization and mapping in autonomous systems.