3D Grid Size Optimization of Automatic Space Analysis for Plant Facility Using Point Cloud Data

Currently, as plant market grows up, client demands to increase construction quality, technology and to reduce construction time. Due to the vast scale of the construction site and complexity of process, plant project can have some problems of communication among the project participants, the duplication of work, errors and rework. To solve this problem, 3D cloud point data of space and equipment is collected by 3D laser scanning. And the space matching is operated to build as-is environment. In the space matching process, data is simplified by using the 3D grid. It is important to select 3D grid size in the algorithm. Data processing speed and error rates depend on the size of 3D grid. But, still there wasn?t the study about optimized 3D grid size. The grid size has been determined only by the user?s experience. This study purpose to define optimized grid-size for 3D grid base space analysis. The specific research target of this study is Indoor plant facility. We followed experiment on these Condition. First, classified plant equipment according to the complexity of shape and capacity. Second, we set the classification equipment again by size of grid. Optimization of 3D grid size derive from comparing the volume of 3D grid and real volume of equipment. Third, compare the volume between grid-based model and real object to verify whether it useful or not. Using this method, makes it possible to apply the automatic space analysis algorithm efficiently. And this research applies to automatic space analysis for plant facility. It is expected to be possible to solve the problems and the differences of reactions for the space rearrangement. However, this research only optimizes indoor equipment in plant. So it is necessary to optimization of various equipment.