# Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
## 3 Data Acquisition System and Case Study Specifications  
The key goal of this study is to demonstrate the viability of using a vehicle-based MLS for point cloud acquisition to derive a wide range of serviceability measures for large MSE walls. An in-house-developed MLS, which is shown in Figure 3, was used for the case study conducted. The MLS has two laser scanners (VUX-1 HA, Riegl, Horn, Austria and ZF Profiler 9012, ZF, Wangen, Germany). These two scanners have been specifically chosen to provide long range scanning and high ranging accuracy through the Riegl VUX-1 HA and ZF profile 9012, respectively. The Riegl VUX-1 HA operates at a maximum range of 150 m (with an accuracy of \(\pm\)5 mm) while the ZF Profiler 9012 operates at a maximum range of 120 m (with an accuracy of \(\pm\)2 mm). Each scanner captures roughly one million points per second [16; 17]. The Riegl and ZF units are 2D ranging systems (i.e., they are equipped with a single laser beam that rotates in a single plane). As a result of this scanning mechanism, a given object point will be scanned only once by a single scanner from a given drive run. Therefore, having two scanners onboard the MLS is recommended to increase point density (i.e., number of points per unit surface area) and increase the extent of the covered area. Scanners type, using either two Riegl or two ZF scanners for example, would not make a difference on the quantitative inspection outcome. Derivation of the point cloud data requires a geo-referencing procedure, which aims at establishing the position and orientation of the individual sensors relative to a user-defined mapping coordinate system. The used position and orientation system in this study comprises a Nov  
Figure 3: The mobile LiDAR mapping system used for the MSE wall data acquisition. ([PERSON] et al. [15]). receiver and ISA-100C near-navigation grade IMU (NovAtel, Calgary, AB, Canada) to directly provide the geo-referencing parameters for the imaging and ranging sensors. Inertial Explorer with Differential GNSS post-processing from NovAtel is used for the integration of the raw GNSS/INS data. Following post-processing, the accuracy of the derived GNSS/INS attitude is 0.003\({}^{\circ}\) and 0.004\({}^{\circ}\) for the pitch/roll and heading (yaw), respectively [18]. The positional accuracy, on the other hand, is in the range between 0.01 to 0.02 m [18]. The derivation of high-quality point cloud from the directly-geo-referenced scanners is contingent on the accuracy of the position and orientation of the vehicle frame and precise estimation of the spatial and rotational offset (collectively known as the mounting parameters) between the GNSS/INS unit and ranging sensors. Using a high quality GNSS/INS unit would ensure reliable evaluation of the vehicle's position and orientation. In this study, a thorough system calibration procedure by [PERSON] et al. [19] was used for estimating the mounting parameters between the laser scanning units and GNSS/INS position and orientation system. The system calibration parameters were estimated by minimizing misalignments between conjugate linear features, planar features, and points that have been captured by different scanners in multiple drive runs. According to the used calibration procedure, the expected alignment between point clouds from different scanners in a given drive run and/or different drive runs should be in the range of 1-2 cm. Using a dual scanner system equipped with high quality GNSS/INS unit, which has undergone a rigorous calibration procedure, would ensure the availability of dense point clouds along the surveyed MSE walls that are conducive to the evaluation of a reliable set of serviceability measures. A smooth MSE wall in the U.S. State of Indiana was selected as a case study in this research to test the capability of the MLS used as well as the feasibility of the proposed processing strategy in deriving the \(\alpha_{L}\) and \(\alpha_{T}\) angular distortions as well as the performance measures introduced by [PERSON] et al. [5].