Enhancement of low-cost UAV-based photogrammetric point cloud using MMS point cloud and oblique images for 3D urban reconstruction
Eunkwan Lee, Soyeon Park, Hyoseon Jang, Wonjun Choi, Hong‐Gyoo Sohn
Abstract
The accurate and dense reconstruction of high-quality 3D spatial information is essential for digital twin-based smart cities. Unmanned aerial vehicle (UAV)-based photogrammetry allows the 3D modeling of urban environments in the shortest possible time. Accurate georeferencing is a prerequisite for utilizing geospatial information. The geolocation accuracy of real-time kinematic (RTK)/post-processing kinematic (PPK) UAV-based photogrammetry is significantly high; however, RTK/PPK UAVs are costly. In contrast, the geolocation accuracy of low-cost UAV-based photogrammetry is generally low. It can be improved by indirect georeferencing using ground control points (GCPs) obtained in situ; however, this requires significant amounts of human resources and time. Therefore, this study analyzes the suitability of utilizing the mobile mapping system (MMS) point cloud as GCPs for low-cost UAV-based photogrammetry. We checked the significance of the vertical distribution of GCPs on the geolocation accuracy of low-cost UAV-based photogrammetry using the feature points of building facades (captured via vehicle-based MMS along roads) as GCPs. In addition, typical UAV-based photogrammetry only uses nadir images, which limits the detailed 3D reconstruction of building facades. In this study, a detailed reconstruction and an improved vertical geolocation accuracy were achieved using oblique images. Experiments demonstrated that the geolocation accuracy of the low-cost UAV-based photogrammetric point cloud improved to within 16 cm in the X-, Y-, and Z-directions. It was at its highest when the GCPs were diversely distributed in the vertical direction. Finally, we generated an enhanced point cloud by merging the low-cost UAV-based photogrammetric point cloud and the MMS point cloud.