|Mirror-aided point cloud data acquisition and scan planning
|Li, Heng (BRE)
Kim, Minkoo (BRE)
Image processing -- Digital techniques
Hong Kong Polytechnic University -- Dissertations
|Department of Building and Real Estate
|xvii, 154 pages : color illustrations
|Over the past decade, point cloud-based 3D reconstruction has gained popularity in the construction industry as reality-captured as-is models obtained from 3D reconstruction provide genuine and accurate geometric and semantic information of target structures. For the 3D reconstruction using point cloud data, a merging process often called the 'registration' process that combines multiple sets of point cloud data acquired at different scan positions is necessary to produce a complete 3D as-is model of the structure. However, the registration process inevitably produces merging errors and it is time-consuming. This thesis presents new approaches to tackle the technical issues of the current registration process in terms of efficiency and accuracy. The core research ideas of this study are to 1) use mirrors to generate a complete point cloud data of target structure; and 2) conduct simulation-based scan planning to increase the efficiency for the point cloud acquisition and processing. For mirror-aided data acquisition technique, registration-free geometric quality inspection (GQI) technique that is capable to scan invisible side surfaces of planar-type elements from the terrestrial laser scanner applying flat mirrors is first developed. Then, the applicability of the mirror-aided registration-free data acquisition method is further optimized by reducing mirror size and rotating the mirror. The overall concept and procedure for conducting the registration-free GOI technique are proposed on the basis of the mirror-reflection principle. The lab-scale tests indicate that the proposed technique provides more accurate GQI results while reducing scanning time compared to the traditional registration methods, demonstrating great potential for application in small-scale planar-type prefabricated construction elements in the construction industry.
Furthermore, a new surface flatness measurement method is displayed to employ flat mirrors to upgrade the measurement range with acceptable measurement accuracy and make possible the scanning of occluded areas even when the laser scanner is out of sight. On the basis of the proposed surface flatness measurement approach, two hypotheses connected with the increment of the scan coverage range and flatness measurement even in occluded areas are proposed. The experimental validations show that the developed mirror-aided technique can increase the measurement accuracy in a long-range area far away from the laser scanner and enable flatness measurement in hidden areas caused by physical barriers such as interior walls. For the simulation-based scan planning method, a new scan planning method is first developed to determine the optimal locations of the laser scanner and flat mirrors for the proposed registration-free data acquisition method applying mirrors. Besides, a scan configuration determination method is developed to determine the optimal laser scanner location for accurate and efficient rebar diameter classification. The geometrical relationship model among the laser scanner and target structures is developed to promote scan planning. Validation tests show that the scan planning methods can provide efficient and accurate mirror-aided registration-free data acquisition and rebar diameter prediction, demonstrating the great potential for the application of the proposed scan planning technique in the manufacturing and construction stage. Overall, this research proposes a mirror-aided point cloud data generation technique using flat mirrors. In addition, simulation-based scan planning method is proposed on the mirror-aided data acquisition technique and rebar diameter classification method. Validation tests show that the mirror-aided point cloud data generation technology and scan planning method based on simulation have great potential for efficient point cloud data collection in the construction industry.
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