Enhancement of low-cost UAV-based photogrammetric point cloud using MMS point cloud and oblique images for 3D urban reconstruction

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.