A pivotal component of the construction industry's transformation involves the utilization of laser scanners to produce highly accurate point clouds, providing an unparalleled representation of real-world structures. Apple's LiDAR technology enables the capture of 3D point cloud data through the handy iPhone LiDAR Sensor.
This study addresses the challenge of enhancing the construction accuracy of utility duct installation with reference to a power cable installation project in Japan. The transition from overhead power lines to underground systems is a significant endeavor, and ensuring precise pipe installation is a complex task. Manually checking the layout of utility ducts and pipes for each step, along with their documentation, requires a significant amount of time, labor, and is susceptible to errors, further exacerbated by limited project timelines and labor shortages in the construction industry.
Our research focuses on developing an automated algorithm to detect and measure the 3D dimensions of boxes, positions, and lengths of pipes on construction sites utilizing iPhone LiDAR technology. In the methodology, site-captured point cloud data of utility ducts using iPhone LiDAR technology is processed to separate the excavation part through plan fitting. Sidewalls of the utility duct are separated from the excavation using normal-based K-means clustering. The bottom slab of the utility duct is removed from the pipes based on color analysis and 3D volume masks. The accuracy of pipe extraction is 83% based on tested data. In the next step, the extracted pipe point cloud is preprocessed through voxel-based down-sampling and then clustered using the DBSCAN clustering tool. The candidate clusters, found after clustering, have been post-processed to segment the point cloud for each pipe. As of now, we have achieved successful segmentation of straight pipes point cloud only.
Our proposed algorithm bridges the construction accuracy gap for utility ducts, promotes digital transformation in the industry, and offers a user-friendly, on-site, mobile phone-operated solution with low computation.
