Producing Usable Simulation Terrain Data from UAS-Collected Imagery (bibtex)
by Ryan Spicer, Ryan McAlinden, Damon Conover
Abstract:
At I/ITSEC 2015, we presented an approach to produce geo-referenced, highly-detailed (10cm or better) 3D models for an area of interest using imagery collected from cheap, commercial-off-the-shelf, multirotor Unmanned Aerial Systems (UAS). This paper discusses the next steps in making this data usable for modern-day game and simulation engines, specifically how it may be visually rendered, used and reasoned with by the physics system, the artificial intelligence (AI), the simulation entities, and other components. The pipeline begins by segmenting the georeferenced point cloud created by the UAS imagery into terrain (elevation data) and structures or objects, including vegetation, structures, roads and other surface features. Attributes such as slope and edge detection and color matching are used to perform segmentation and clustering. After the terrain and objects are segmented, they are exported into engine-agnostic formats (georeferenced GeoTIFF digital elevation model (DEM) and ground textures, OBJ/FBX mesh files and JPG textures), which serves as the basis for their representation in-engine. The data is then attributed with metadata used in reasoning – collision surfaces, navigation meshes/networks, apertures, physics attributes (line-of-sight, ray-tracing), material surfaces, and others. Finally, it is loaded into the engine for real-time processing during runtime. The pipeline has been tested with several engines, including Unity, VBS, Unreal and TitanIM. The paper discusses the pipeline from collection to rendering, and as well as how other market/commercially-derived data can serve as the foundation for M&S terrain in the future. Examples of the output of this research are available online (McAlinden, 2016).
Reference:
Producing Usable Simulation Terrain Data from UAS-Collected Imagery (Ryan Spicer, Ryan McAlinden, Damon Conover), In Proceedings from the Interservice/Industry Training, Simulation and Education Conference (I/ITSEC) 2016, National Training and Simulation Association, 2016.
Bibtex Entry:
@inproceedings{spicer_producing_2016,
	address = {Orlando, FL},
	title = {Producing {Usable} {Simulation} {Terrain} {Data} from {UAS}-{Collected} {Imagery}},
	url = {http://ict.usc.edu/pubs/Producing%20Usable%20Simulation%20Terrain%20Data%20from%20UAS-Collected%20Imagery.pdf},
	abstract = {At I/ITSEC 2015, we presented an approach to produce geo-referenced, highly-detailed (10cm or better) 3D models for an area of interest using imagery collected from cheap, commercial-off-the-shelf, multirotor Unmanned Aerial Systems (UAS). This paper discusses the next steps in making this data usable for modern-day game and simulation engines, specifically how it may be visually rendered, used and reasoned with by the physics system, the artificial intelligence (AI), the simulation entities, and other components. The pipeline begins by segmenting the georeferenced point cloud created by the UAS imagery into terrain (elevation data) and structures or objects, including vegetation, structures, roads and other surface features. Attributes such as slope and edge detection and color matching are used to perform segmentation and clustering. After the terrain and objects are segmented, they are exported into engine-agnostic formats (georeferenced GeoTIFF digital elevation model (DEM) and ground textures, OBJ/FBX mesh files and JPG textures), which serves as the basis for their representation in-engine. The data is then attributed with metadata used in reasoning – collision surfaces, navigation meshes/networks, apertures, physics attributes (line-of-sight, ray-tracing), material surfaces, and others. Finally, it is loaded into the engine for real-time processing during runtime. The pipeline has been tested with several engines, including Unity, VBS, Unreal and TitanIM. The paper discusses the pipeline from collection to rendering, and as well as how other market/commercially-derived data can serve as the foundation for M\&S terrain in the future. Examples of the output of this research are available online (McAlinden, 2016).},
	booktitle = {Proceedings from the {Interservice}/{Industry} {Training}, {Simulation} and {Education} {Conference} ({I}/{ITSEC}) 2016},
	publisher = {National Training and Simulation Association},
	author = {Spicer, Ryan and McAlinden, Ryan and Conover, Damon},
	month = nov,
	year = {2016},
	keywords = {ARL, MxR, STG, UARC}
}
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