Xianquan Han

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About

Xianquan Han is a leading researcher at the intersection of building information modeling (BIM), indoor spatial semantics, and autonomous robotics. His most impactful work, "Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning" (2024, 18 citations), pioneers a framework that bridges static BIM data with dynamic robotic path planning. By integrating IndoorGML standards, Han enables quadruped robots to interpret building semantics—such as room functions and doorway constraints—for intelligent navigation and automated 3D scanning of complex indoor environments. This contribution directly addresses the critical gap between digital building models and real-world robotic deployment, offering a scalable solution for facility management, construction monitoring, and disaster response. His research has garnered attention for its practical implications in smart building automation and digital twin technologies. Han’s work exemplifies how semantic enrichment can transform BIM from a passive repository into an active, machine-readable environment, setting a foundation for next-generation autonomous systems in the built environment.