Implementation of Rapid As-built Building Information Modeling Using Mobile LiDAR

The need for development of reliable and efficient real-time data acquisition systems recently has attracted a great deal of attention in the construction industry, basically because of the demands for highly frequent updates in most visualization, optimization, and coordination-related applications. The predominant data that have been used in the construction industry so far is rather less accurate. Moreover, the conventional methods of data acquisition are based on fieldwork that is time consuming, expensive, and labor intensive. Accuracy of original data and efficiency of data acquisition could be enhanced using new LiDAR technologies. LiDAR is the advanced remote sensing technology that is able to provide 3D data with centimeter to millimeter level accuracy effectively and efficiently. However, the implementation of 3D data for accurate as-built creation is still challenging, especially for openings and fine details of the construction objects in an indoor environment. This paper presents a framework for rapid as-built modeling using 3D point cloud data captured by a handheld LiDAR The procedure involves five key stages from data capturing to create a final model. This paper reports the implementation of the framework using the state-of-the-art mobile LiDAR to analyze fine details of a sample building. LiDAR data of a sample building in an indoor environment is captured using a mobile laser scanner and is analyzed after registration and segmentation processes. The reconstructed model using the as-built data is compared with the existing 2D AutoCAD plans of the sample building and the traditional measurements to verify the accuracy of the proposed method. The results of this ongoing study confirm that the proposed model development technique can serve as a reliable tool for accurate development of rapid as-built building models (rABM). The accuracy ranges from 5 to 30 mm, depending on the object size and position. The proposed algorithm was shown to be highly efficient in identifying the main visible components in the buildings.