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.
@article{arxiv.2508.20034,
title = {FlyMeThrough: Human-AI Collaborative 3D Indoor Mapping with Commodity Drones},
author = {Xia Su and Ruiqi Chen and Jingwei Ma and Chu Li and Jon E. Froehlich},
journal= {arXiv preprint arXiv:2508.20034},
year = {2025}
}
Comments
Accepted at UIST 2025, 14 pages, 8 figures, 2 tables