A divide-and-conquer algorithm for 3D imaging planning in dynamic construction environments Zhang, Cheng; Tang, Pingbo
On construction sites, engineers need real-time geometries of workspaces for reducing spatial conflicts between construction activities, monitoring construction progress, and controlling construction quality. The dynamic nature of construction environments, however, poses challenges to collecting sufficient and high-quality geometric data to support such needs. Effective uses of advanced 3D-imaging technologies also rely on engineers’ experiences. Manual imagery data collection often results in missing or low-quality data or unnecessary over-detailed inspection and exponentially long computation time, which may bring extra cost to construction project. To overcome these challenges of 3D data collection, the study presented in this paper proposes a new automatic 3D imaging planning method for guiding efficient and effective 3D imagery (e.g., LiDAR) data collection in dynamic environments. This method first establishes a sensor model of laser scanners, and then establishes a divide-and-conquer algorithm for achieving rapid and precise 3D imaging planning for dynamic construction site. For a given jobsite, this algorithm creates a graph that represent objects or features having specific data quality requirements (e.g., level of accuracy, and level of detail) as nodes, and spatial relationships between these objects as edges (e.g., distance, line-of-sight). A graph-coloring algorithm decomposes such a graph into sub-graphs for finding their “local” optimal 3D imaging plans. A solution aggregation algorithm then combines those “local” optimal data collection plans into a complete 3D imaging plan for the complete graph representing the scene. Testing results indicate that the divide-and-conquer algorithm can improve the performance and computational efficiency of 3D imaging planning and producing better results than the conventional 3D imaging planning methods.
Item Citations and Data
Attribution-NonCommercial-NoDerivs 2.5 Canada