A Quantitative Investigation of the Effect of Scan Planning and Multi-Technology Fusion for Point Cloud Data Collection on Registration and Data Quality: A Case Study of Bond University’s Sustainable Building

Abstract

The construction industry requires comprehensive and accurate as-built information for a variety of applications, including building renovations, historic building preservation and structural health monitoring. Reality capture technology facilitates the recording of as-built information in the form of point clouds. However, the emerging development trends of scan planning and multi-technology fusion in point cloud acquisition methods have not been adequately addressed in research regarding their effects on point cloud registration quality and data quality in the built environment. This study aims to extensively investigate the impact of scan planning and multi-technology fusion on point cloud registration and data quality. Registration quality is evaluated using registration error (RE) and scan overlap rate (SOR), representing registration accuracy and registration coincidence rate, respectively. Conversely, data quality is assessed using point error (PE) and coverage rate (CR), which denote data accuracy and data completeness. Additionally, this study proposes a voxel centroid approach and the PCP rate to calculate and optimize the CR, tackling the industry’s challenge of quantifying point cloud completeness.

Exploring the Impact of Different Registration Methods and Noise Removal on the Registration Quality of Point Cloud Models in the Built Environment: A Case Study on Dickabrma Bridge

“…This scanner offers point cloud collection speeds of up to 614 m at 0.5 million points per second and 307 m at 1 million points per second. The overall accuracy of the scanner can generally reach within ±2 mm [27,54]. We implemented a total of six PCD registration algorithms and computed two types of metrics.…”

“…In previous studies, it has been substantiated that the quality of point cloud registration can significantly affect the accuracy of the resultant point cloud model [27]. The current research primarily employs two metrics, namely RE and TC, to assess the quality of point cloud registration tasks.…”

“…Significant registration errors can lead to a decrease in the overall accuracy of the point cloud model. Even minor errors can gradually accumulate into larger ones throughout multiple continuous scans and iterative registration processes, thereby impacting the overall accuracy [27,28]. This is especially pertinent in bridge engineering, which often involves many successive and adjacent scans in the same direction.…”