Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
This work addresses the need for realistic virtual environments in training and simulations, such as mission planning and threat detection, by providing automated semantic data extraction, though it appears incremental as it builds on prior photogrammetry and simulation tools.
The research tackled the lack of semantic information in automatically reconstructed 3D meshes from UAS photogrammetry by developing a fully automated framework for segmentation and object information extraction, enabling features like replacing 3D mesh trees with geo-typical models and enhancing pathfinding with ground material data.
Our previous works have demonstrated that visually realistic 3D meshes can be automatically reconstructed with low-cost, off-the-shelf unmanned aerial systems (UAS) equipped with capable cameras, and efficient photogrammetric software techniques. However, such generated data do not contain semantic information/features of objects (i.e., man-made objects, vegetation, ground, object materials, etc.) and cannot allow the sophisticated user-level and system-level interaction. Considering the use case of the data in creating realistic virtual environments for training and simulations (i.e., mission planning, rehearsal, threat detection, etc.), segmenting the data and extracting object information are essential tasks. Thus, the objective of this research is to design and develop a fully automated photogrammetric data segmentation and object information extraction framework. To validate the proposed framework, the segmented data and extracted features were used to create virtual environments in the authors previously designed simulation tool i.e., Aerial Terrain Line of Sight Analysis System (ATLAS). The results showed that 3D mesh trees could be replaced with geo-typical 3D tree models using the extracted individual tree locations. The extracted tree features (i.e., color, width, height) are valuable for selecting the appropriate tree species and enhance visual quality. Furthermore, the identified ground material information can be taken into consideration for pathfinding. The shortest path can be computed not only considering the physical distance, but also considering the off-road vehicle performance capabilities on different ground surface materials.