## 1 Introduction  
A mobile mapping system (MMS) can be defined as an integrated sensing system for the collection of geospatial data from a moving platform. At its core, an MMS comprises an integrated GNSS/IMU system for determination of the platform state and one or more imaging sensors. It may include additional navigation sensors such as a wheel revolution counter. The trajectory estimates from the navigation system allow direct georeferencing of acquired image data. The two main types of imaging sensors found on board MMSs are passive cameras and laser scanners. Since the focus of this work is the latter, an MMS is henceforth understood to mean a laser scanning based system. Early MMSs were developed for land-vehicle platforms but have since evolved into backpack, hand cart, water vehicle and unmanned aerial vehicle (UAV) embodiments in support of a vast range of applications. The work described herein focuses on a land-vehicle MMS accuracy testing, though the methods are expected to be broadly applicable to other platforms such as UAVs with suitable adaptation. According to [1], MMSs may be classified according to the accuracy they can achieve, either mapping or survey grade. Achieving the higher, survey-grade accuracy relies on accurate trajectory determination, which is particularly challenging in GNSS-denied and multipath environments such as urban canyons. [PERSON] and [PERSON] [1] report a post-mission quality assurance procedure for survey-grade MMSs. They perform a fully automated self-calibration to estimate corrections to system parameters (boresight and -level arm) and corrections to the trajectory. Planar features are used for the self-calibration. The plane-based system calibration method has been used for airborne [2], UAV [3] and mobile [4; 5] laser scanner systems and the modelling has been extended to include catenary curves [6] and poles [7]. The trajectory model reported by [1] includes a second-order corrections as a function of time. The trajectory is segmented according to position accuracy to allow piecewise modelling with continuity and smoothness constraints. Methods to assess and improve the accuracy of MMSs have been investigated by many authors. Signalized control points [8] and signalized tie points [9] observed in multiple MMS passes have been reported to correct navigation system errors while check points [10] can be employed to independently quantify the errors. The use of total stations and GNSS equipment results in accurately determined point coordinates [11]. However, point-based assessment suffers from the great disparity in data density between the MMS and the independently surveyed points. In recognition of this shortcoming, a number of researchers have developed outdoor test fields for MMS accuracy assessment. Generally speaking, they comprise a dense reference point cloud of a large urban area through which the MMS can be driven. The reference point cloud is collected by static terrestrial laser scanning (TLS) that is georeferenced with static or RTK GNSS methods. The accuracy assessment of MMS data can be performed by comparing the two point clouds, though the features and methods used for comparison vary. [PERSON] et al. [12] detail the testing of several MMSs over a 1.7 km loop. They quantify system accuracy by comparing the coordinates of independently surveyed point features. The reference targets are poles, building corners and curb corners extracted from the point cloud. [PERSON] et al. [13] report a mobile laser scanner test field of similar size (1000 m \(\times\) 550 m area). Their assessment is conducted with 45 control points. [PERSON] and [PERSON] [14] also use TLS data as a reference to assess the accuracy of an MMS. Their assessment is performed using a large number of check points and the lengths of building features. In their manual for laser-based mobile mapping, [PERSON] et al. [15] report two test sites (urban and rural) established with control points for performance testing of MMSs. Both sites are about 3 km long.