# Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
## 4 Methodology  
### Proposed Framework of the Suggested Methodology  
The overall framework for the data collection/processing, and estimation of the serviceability measures is illustrated in Figure 7. In this framework, the standard serviceability measures, as specified by AASHTO [6], include the \(\alpha_{L}\) and \(\alpha_{T}\) angular distortions. In addition, the serviceability measures introduced by [PERSON] et al. [5] including the differential displacements and rotations of the individual panels relative to a \(LF_{cs}\) are also evaluated. Figure 5: Illustrations of (**a**) drive run configuration for the MLS data acquisition and (**b**) 3D point cloud colored by height of the MSE wall faces covered by the MLS. Figure 6: Point cloud at the MSE wall site location collected by the MLS (colored by height). To derive the serviceability measures, the point clouds acquired by the scanners onboard the MLS from different drive runs are registered to a single reference frame. In theory, one can argue that this registration step is redundant for point clouds acquired by an MLS equipped with a direct geo-referencing GNSS/INS unit. In other words, the position and orientation of the laser scanners relative to the mapping frame are available through the combination of the GNSS/INS trajectory with the system calibration parameters--e.g., the horizontal and vertical point cloud coordinates could be defined relative to a Universal Transverse Mercator coordinate system (UTM) with the WGS84 as the datum and the National American Vertical Datum of 1988 (NAVD 88), respectively. Thus, acquired point clouds from neighboring drive runs should be well-aligned provided that accurate system calibration parameters and reliable trajectory data are available. However, the GNSS/INS trajectory might be compromised due to GNSS signal loss of lock when traveling in tunnels and/or under overhead bridges, canopy cover, multipath interference from nearby traffic, and platform speed. Such issues together with residual artifacts in the system calibration parameters can lead to discrepancies between conjugate features in overlapping point clouds from the onboard two scanners and neighboring drive runs. Before taking advantage of the complementary point cloud data from dual scanners and multiple drive runs (e.g., point clouds with higher point density and less occlusions), alignment of such data must be ensured. Considering the accuracy of the alignment procedure, registration strategies can be categorized into coarse and fine approaches [22, 23]. As the name suggests, coarse registration strategies are suited for point clouds that are given relative to reference frames with significant translational and rotational offsets. Fine strategies, on the other hand, are used for coarsely-registered point clouds to ensure better alignment of corresponding features. It is worth noting that this study is focusing on MLS point cloud data, which should be relatively well-aligned through the onboard GNSS/INS unit coupled with the system calibration procedure. Therefore, this research will implement a fine registration strategy, which is discussed in detail in Section 4.2. For both standard and newly-available serviceability measures, one needs to partition an MSE wall facade into planar sections, denoted here forward as MSE wall faces. The identification of individual planar sections within the wall facade starts with the finely-registered point cloud. Thus, if an MSE wall has a multi-face facade with individual planar faces, as shown in Figure 2a, or piece-wise planar  
Figure 7: Overall acquisition and processing framework for the proposed methodology. facade, as shown in Figure 2b, it needs to be partitioned into sections that are believed to be perfectly planar. In this study, the MSE wall facades are manually partitioned. The partitioning procedure is based on the Root Mean Square Error (RMSE) of the normal distances between the points along a partitioned face from the corresponding best-fitting plane through that face.