Papers
3
Total Citations
80
H-Index
3
About
Xiaoping Zhou is a leading researcher in indoor spatial intelligence, whose work bridges building information modeling (BIM), 3D mapping, and autonomous navigation. Her core contributions lie in developing automated methods to extract and generate accurate 3D indoor maps and navigation networks from BIM data—a critical enabler for indoor location-based services, robotics, and computer-aided design. Her most cited paper, "Accurate and Efficient Indoor Pathfinding Based on Building Information Modeling Data" (2020, 69 citations), addresses the fundamental challenge of indoor pathfinding by leveraging BIM to create reliable maps where none previously existed. She further advanced the field with "Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data" (2021) and "Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning" (2023), which introduced novel techniques for automating the labor-intensive process of modeling indoor navigation networks. Zhou’s work has significant implications for robotics, 3D GIS, and smart building applications, reducing reliance on manual modeling and enabling scalable, precise indoor navigation solutions. Her research continues to shape how autonomous systems and location-based services understand and traverse complex indoor environments.
Research Focus
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Top Papers
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