Automatic Space Analysis Using Laser Scanning and a 3D Grid: Applications to Industrial Plant Facilities

While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, etc.,). Furthermore, problems during installation, operation, and maintenance stages caused by inconsistencies between floor plans and actual layout are on the rise. Although some of these conflicts can be addressed through clash detection using BIM (building information modeling), quality BIM models are scarce, especially for existing industrial plants. This study proposes a way to address the complexities caused by changes during plant construction and securing space for the installation of equipment during the construction and lifecycle of built facilities. 3D cloud point data of space and equipment were collected using 3D laser scanning to conduct space matching. In processing the space matching, data were simplified by applying the 3D grid and by comparing the data, easier identification of the space for target equipment was accomplished. This study also proposed a pre-processing method based on sub-sampling that optimizes the point cloud data and verifies the processing speed and accuracy. Lastly, it finds free space for various equipment layouts required in industrial plant projects by space analysis, proposed algorithms, and processes for obtaining the coordinates of valid space for equipment arrangement. The proposed method of this study is expected to help solve the problems derived from arrangement and installation of new equipment in a complex plant site.

[1]  Burcu Akinci,et al.  Automated Recognition of 3D CAD Objects in Site Laser Scans for Project 3D Status Visualization and Performance Control , 2009 .

[2]  Hyun-Seok Yoo,et al.  Object Detection From 3D Terrain Data Gener Ated by Laser Scanner of Intelligent Excavating System(IES) , 2011 .

[3]  Soohee Han.  Design of Memory-Efficient Octree to Query Large 3D Point Cloud , 2013 .

[4]  Soon-Wook Kwon,et al.  Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3D world model for smart heavy equipment planning , 2019, Automation in Construction.

[5]  Yong Xiao,et al.  Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites , 2015 .

[6]  Young Dae Lee,et al.  A Study on the Waste Volume Calculation for Efficient Volume Monitoring in a Landfill Facility , 2012 .

[7]  Naai-Jung Shih,et al.  Point-Cloud-Based Comparison between Construction Schedule and As-Built Progress: Long-Range Three-Dimensional Laser Scanner’s Approach , 2004 .

[8]  George Vosselman,et al.  Knowledge based reconstruction of building models from terrestrial laser scanning data , 2009 .

[9]  Soohee Han,et al.  Productive high-complexity 3D city modeling with point clouds collected from terrestrial LiDAR , 2013, Comput. Environ. Urban Syst..

[10]  Frédéric Bosché,et al.  Automated retrieval of 3D CAD model objects in construction range images , 2008 .

[11]  Soohee Han,et al.  Automated and Efficient Method for Extraction of Tunnel Cross Sections Using Terrestrial Laser Scanned Data , 2013, J. Comput. Civ. Eng..

[12]  Shuowen Hu,et al.  Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications , 2016 .

[13]  Jun Wang,et al.  Geotechnical and safety protective equipment planning using range point cloud data and rule checking in building information modeling , 2015 .

[14]  Andreas Nüchter,et al.  One billion points in the cloud – an octree for efficient processing of 3D laser scans , 2013 .

[15]  Julien Réthoré,et al.  Extraction of stress intensity factors for 3D small fatigue cracks using digital volume correlation and X-ray tomography , 2015 .

[16]  Soon-Wook Kwon,et al.  Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds , 2004 .

[17]  Ian D. Reid,et al.  Recognition of parameterized objects from 3D data: a parallel implementation , 1994, Image Vis. Comput..

[18]  T. Mikita,et al.  Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment , 2015 .

[19]  Christian Früh,et al.  Data Processing Algorithms for Generating Textured 3D Building Facade Meshes from Laser Scans and Camera Images , 2005, International Journal of Computer Vision.