Mengyun Xu

Papers

Total Citations

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About

Mengyun Xu is a robotics researcher whose work centers on advancing probabilistic pose estimation—a critical capability for applications like registration, hand–eye calibration, and simultaneous localization and mapping (SLAM). Her major contribution lies in developing novel filtering methods that move beyond traditional Gaussian uncertainty models. Specifically, her 2018 paper, “Probabilistic pose estimation using a Bingham distribution-based linear filter” (30 citations), and her 2017 work, “Bingham Distribution-Based Linear Filter for Online Pose Estimation” (21 citations), introduce a framework that leverages the Bingham distribution to more accurately represent rotational uncertainty in pose parameters. This approach addresses a fundamental limitation of Gaussian-based methods, which can be inadequate for describing the non-Euclidean nature of rotations. By enabling more robust and reliable online pose estimation, Xu’s work has direct implications for improving the performance and safety of autonomous systems. Her research is particularly notable for bridging theoretical probabilistic modeling with practical, real-time robotics applications, making her a key contributor to the field of robotic perception and state estimation.