# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 1 Introduction  
Point clouds are widely used to generate as-built building information models (BIM). These point clouds can be acquired using various technologies, such as laser scanners or stereo cameras [1, 2, 3]. Among these technologies, terrestrial laser scanners (TLS) have been actively employed in Scan-to-BIM applications due to their high accuracy [4, 5]. However, acquiring comprehensive point clouds with TLS is both labor-intensive and time-consuming, as it requires multiple setup positions to capture the entire construction site. Indoor environments, in particular, demand a greater number of scanning positions than outdoor settings, further increasing the acquisition time. To address these limitations, mobile laser scanners (MLS) have been adopted for capturing indoor construction sites [6, 7]. Unlike TLS, MLS can perform continuous scanning at a higher speed by leveraging simultaneous localization and mapping (SLAM) technology. Despite this advantage, MLS systems generally offer lower accuracy, as SLAM-based scanning is inherently less precise than TLS-based methods [7, 8]. These inaccuracies can significantly affect various construction applications, including inspection, quantityestimation, augmented/virtual reality (AR/VR), digital twin development, automation, and robotics. To overcome these limitations, several enhanced MLS devices have been developed by integrating TLS and SLAM technology [9], or incorporating GNSS technology and highly precise IMU sensor [10]. However, these devices remain expensive, and GNSS use is constrained indoor environments. [PERSON] et al. [7] used MLS to evaluate rebar spacing and quality. The device used was the GeoSLAM ZEB-Horizon, which has a relative error up to 6 mm [11]. [PERSON] et al. [12] used the GeoSLAM ZEB-Revo for as-built BIM generation, with a reported relative accuracy of \(\pm\) 10-30 mm [13]. [PERSON] et al. [14] also used GeoSLAM ZEB-Revo for 3D reconstruction. [PERSON] et al. [15] generated as-built BIM from MLS point clouds captured using Heron MX Twin Color scanner, which has a global accuracy of less than 20 mm [16]. Consequently, MLS products widely adopted for indoor construction scanning still deliver lower accuracy than TLS. These studies demonstrate the expanding application of MLS in Scan-to-BIM. However, most of the cited MLS devices exhibit relatively low accuracy. Moreover, existing studies utilizing MLS point clouds in BIM applications have paid limited attention to the inherent inaccuracies in MLS point clouds. Accordingly, a thorough characterization of these inaccuracies, along with the development of effective mitigation strategies, could play a critical role in ensuring their reliable application. Nonetheless, previous research on MLS accuracy has rarely addressed such error distribution patterns. Furthermore, studies employing MLS for the generation of as-built BIM models have typically overlooked these device-specific biases in their methodologies. In other words, current approaches often apply algorithms such as random sample consensus (RANSAC) without accounting for these deviations, which may significantly impact the overall geometric accuracy of the resulting as-built BIM. Meanwhile, for the correction of MLS point clouds, previous studies have explored approaches that utilize geometric features for self-calibration during real-time generation of complete MLS point clouds [17], as well as machine learning-based methods [18]. These studies focus on the development of more advanced MLS. While they contribute to improving the accuracy of MLS, they do not address the cumulative error distribution patterns that may occur during actual use. Therefore, we designed a method to analyze error distribution patterns of MLS point clouds and conducted experiments for three MLS devices using this method. Additionally, this study verified that the obtained error pattern could serve as a correction factor to improve geometric accuracy of as-built BIM models. This study is based on two key assumptions.