## 4 Discussion  
As the results show, the tested MLS systems are capable of acquiring accurate point cloud data under good GNSS coverage conditions. Buildings, trees and other structures often cause disturbances in satellite visibility. Moreover, the performance of other navigation instruments, such as IMUs and odometers as well as post-processing algorithms, defines the achievable accuracy. Tools for trajectory accuracy improvement are being developed and new satellites are being launched, which should improve accuracy in areas where the current systems have problems. Even though the computation of the sensor's driving path and orientation results in observation (GNSS, IMU) errors being minimized, there are still errors in laser distance measurement, in scanning mirrors, in position (GNSS) and in orientation (IMU). Consequently, there are systematic offsets and random variations both in plane and height [41]. These errors can be minimized by means of strip adjustment, which is earlier known from ALS (e.g., TerraMatch), and which requires repeated measurements of the same surfaces and objects. With MLS, the possible objects and surfaces, which can be used in the correction process, include elevation model, paintings in the pavement, vertical poles and building corners. Imperfect boresight calibration, in addition to the navigation errors, between the scanners on a multi-scanner systems leads to multiple reproductions of objects, as in Figure 19. These kinds of errors in the relative orientation of the instruments lead to errors in the measured point clouds, which can cause problems in the continued processing of the data, such as extraction and modeling of objects. Systematic offset errors between the sensors (\(\Delta\)X, \(\Delta\)Y, \(\Delta\)Z) can be detected using observations of common objects close to them. For example, paintings on the road surface are feasible for such analysis. The \(\Delta\)roll error can be detected using the elevation model acquired with multiple surveys; \(\Delta\)pitch and \(\Delta\)heading errors can be detected with vertical objects such as poles and building corners. These systematic errors can be corrected appropriately by even manual processing of the data. The time-dependent variation of these data (random part) needs larger numbers of observations for corrections, which calls for the development and use of more automated techniques. Since the results produced by all of the systems were adequate for high level of roadside mapping, the main focus in MLS quality research should be on the improvement of the trajectory solution under non-ideal (and also with ideal) conditions using both improved hardware (additional sensors) and software solutions (post-processing). As regards the hardware, the commercially available systems already include odometers. Additional sensors improving the georeferencing/localization solution during GNSS signal loss should be included. Examples of these additional sensors are cameras, whose data are automatically processed to solve the position changes together with IMU and odometer when GNSS signal is not appropriate. Alternative solutions include the use of signals, e.g., painted signals on the road surfaces, as has been demonstrated by [PERSON] [42] in connection with the Helsinki tram MLS survey. The MLS data for comparison was obtained by applying the standard process of the data providers. Laboratory calibration of offsets and orientations of system components can be fine-tuned by the real-live data. In Optech and Streetmapper both of these calibrations are assumed to have been done already by the data provider. With RIEGL and ROAMER, the calibration was done first with by using only the laboratory-type calibration, which showed that there was a need for further calibration in the field. The second point cloud was provided taking the aspects mentioned in Section 2.1.3 into account, and (for example) in ROAMER a significant roll error was found with overlapping point cloud data.