Lingwen Zeng

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About

Lingwen Zeng is a leading researcher in spatial data science and intelligent navigation, with a primary focus on indoor 3D reconstruction and robotic perception. Their most influential work introduces a novel indoor structure extraction (ISE) method that transforms dense point clouds into actionable maps for autonomous robot navigation. By developing a two-staged region growing algorithm, Zeng’s approach generates both a feature structure map (FSM) for detailed planar reconstruction and a navigation structure map (NSM) for efficient path planning—a dual-map framework that bridges the gap between raw sensor data and real-world robotic applications. This foundational paper has garnered 13 citations, underscoring its significance in advancing indoor mapping technologies. Zeng’s contributions are particularly notable for addressing the long-standing challenge of extracting meaningful structural features from cluttered, real-world environments, enabling robots to navigate complex indoor spaces with greater accuracy and reliability. Their work continues to influence the fields of computer vision, autonomous systems, and spatial intelligence, making Zeng a key figure in the evolution of indoor robotic navigation.