Papers
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H-Index
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About
Hye-Min Won is a researcher advancing the frontier of reliable robotic navigation through uncertainty-aware perception systems. Her primary research focuses on multi-modal localization for autonomous robots operating in complex indoor environments, where sensor noise and environmental ambiguity pose significant challenges. In her landmark work, "Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization," Won introduces a novel percentile-based rejection framework that enhances localization reliability without altering the underlying prediction model. This contribution directly addresses a critical gap in robotics: ensuring trustworthy localization outputs when faced with uncertain or conflicting sensor data. While her most-cited paper currently holds 1 citation, reflecting its recent publication in 2025, the work represents a foundational step toward safer, more robust autonomous navigation. Won’s approach has immediate implications for service robots, warehouse automation, and assistive technologies, where localization failures can lead to system failures or safety risks. Her research sits at the intersection of probabilistic robotics, sensor fusion, and uncertainty quantification, offering practical solutions that bridge theoretical advances with real-world deployment needs. As the field increasingly prioritizes reliability over raw performance, Won’s contributions are poised to influence next-generation localization systems.
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Top Papers
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