## 1 Introduction
Accuracy is assessed with signalized control points and TLS reference data. The latter source is used at a specific site to estimate bridge clearance. [PERSON] et al. [16] use TLS reference data collected of two buildings in a city center to evaluate MMS accuracy. Iterative closest point (ICP) method registration [17] is performed on segmented building data and various statistical measures, including higher-order moments, are computed for the assessment. [PERSON] et al. [18] constructed a 2.5 km long TLS point cloud of a medieval wall for the accuracy assessment of their MMS. However, the assessment was performed over a small section of the wall. The accuracy assessment of indoor MMSs (IMMS) is described by [19]. Three IMMSs were tested using a pre-surveyed cultural heritage site as a reference test field. TLS data of the test field serves as ground truth. IMMS data were collected on two pre-defined paths: one closed and one open. Subsets of the data were registered to reference data by the ICP method. A number of different data comparisons are reported to quantify the accuracy of each system. The assessment of MMS accuracy is not, however, confined to the built environment. [PERSON] et al. [20] evaluate the quality of backpack laser MMS in a forest relative to reference data collected with UAV and TLS. The evaluation is performed by comparing data patches comprising 10 s of data with ICP. The time series duration is a tradeoff between enough topographic variation in the data and changes in system orientation. Performing independent ICPs creates discontinuities between patches, so the parameters of adjacent patches are (linearly) time dependent. The past decade has seen tremendous growth in the utilization of unmanned aerial vehicles for airborne mapping. Though cameras are ubiquitous sensors found onboard mapping UAVs, the deployment of light-weight laser scanner systems is becoming more commonplace. Accordingly, the assessment of the accuracy of point clouds derived from such systems is of great importance. Perhaps not surprisingly, the use of an independently observed reference point cloud has been employed by a number of researchers. Several different laser scanner systems appear in the literature including the ibeo LUX LiDAR sensor [21], the SICK LMS511 PRO [22], the RIEGL miniVUX lidar [23] as well as the HDL-32E [24], the VLP-16 Puck HI-RES [3] and the VLP-16 [25] models from Velodyne. [PERSON] et al. [21] report on the assessment of UAV point cloud accuracy in support of levee monitoring using signalized points and cross sections. This group utilizes airborne laser scanner and ground survey data for a later assessment [25]. A multi-faceted assessment of UAV laser scanner data quality over a landslide area is described by [23]. Assessments of strip adjustment quality, overall digital elevation model accuracy, point density and change detection were conducted by comparing the UAV data with TLS, total station and GNSS data. The use of TLS point clouds of single buildings and their surroundings as the reference data are reported by [22; 24]. Clearly, recent activities found in the literature indicate the importance of evaluating the metric performance of lightweight laser scanning systems utilized in MMSs regardless of whether they are deployed on a land vehicle or a UAV platform. This need is further emphasized with the increased use of low-cost microelectromechanical systems (MEMS) sensors in the navigation system. This work presents the design of a completely automated framework for land-vehicle MMS accuracy assessment by comparing a mobile laser scanner point cloud with a reference point cloud of a dedicated testing facility. The establishment of the testing facility comprising several downtown city blocks--a challenging GNSS environment--is also detailed. Multiple datasets with an MMS fitted with a Velodyne VLP-16 and three different MEMS sensors were captured to demonstrate the effectiveness of the proposed method. Detailed evaluation of these sensors is presented following the experiment description.