FlyMeThrough: Human-AI Collaborative 3D Indoor Mapping with Commodity Drones

FlyMeThrough: Human-AI Collaborative 3D Indoor Mapping with Commodity Drones Xia Su, Ruiqi Chen, Jingwei Ma, Chu Li, Jon E. Froehlich UIST 2025: The 38th Annual ACM Symposium on User Interface Software and Technology Session: 2. Interacting in the Real World Indoor mapping data is crucial for routing, navigation, and building management, yet such data are widely lacking due to the manual labor and expense of data collection, especially for larger indoor spaces. Leveraging recent advancements in commodity drones and photogrammetry, we introduce FlyMeThrough---a drone-based indoor scanning system that efficiently produces 3D reconstructions of indoor spaces with human-AI collaborative annotations for key indoor points-of-interest (POI) such as entrances, restrooms, stairs, and elevators. We evaluated FlyMeThrough in 12 indoor spaces with varying sizes and functionality. To investigate use cases and solicit feedback from target stakeholders, we also conducted a qualitative user study with five building managers and five occupants. Our findings indicate that FlyMeThrough can efficiently and precisely create indoor 3D maps for strategic space planning, resource management, and navigation. DOI:: doi.org/10.1145/3746059.3747777 Web:: https://programs.sigchi.org/uist/2025... Video presentations for UIST 2025 papers