Fang Wan

Papers

Total Citations

H-Index

About

Fang Wan is a researcher specializing in robotics perception, 3D scene understanding, and autonomous navigation, with a particular focus on advancing the capabilities of intelligent robotic systems in complex real-world environments. Their work addresses critical challenges in how robots perceive, map, and reason about their surroundings, pushing the boundaries of what autonomous systems can achieve in large-scale, dynamic settings. Among Wan's most notable contributions is their development of a multisensor fusion approach to 3D reconstruction, which tackles the well-documented limitations of traditional RGBD camera-based Visual Simultaneous Localization and Mapping (VSLAM) algorithms — particularly their struggles with accuracy and range in expansive indoor environments. This work, which has garnered 7 citations since its 2020 publication, offers a meaningful step forward for real-time robotic mapping. Complementing this, Wan's research on constructing structured semantic 3D scene graphs using a bottom-up framework addresses the nuanced challenge of enabling robots to parse and reason about complex 3D environments for sophisticated human-robot interaction — accumulating 5 citations since publication. Wan's body of work reflects a coherent and ambitious research vision: equipping autonomous robots with richer, more reliable environmental understanding to support next-generation intelligent systems.