# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 5 Results
### Multi-Scanner Approach  
Most modern MLS systems with two scanners allow for separate exportation of the point clouds. We, therefore, recommend first analyzing how well the point clouds of the left and right scanners agree before proceeding with further analysis. The reasons for a possible mismatch of the point clouds are manifold. If the boresight angles and lever arms are insufficiently calibrated, the point clouds inevitably do not match. In the case of asymmetrically mounted dual scanner systems, the scan geometry is different for the two scanners, resulting in systematic deviations in the laser measurements. The quality of the trajectories can also affect the point cloud agreement, especially if the quality changes significantly or the period between scanner samplings is long. Point-to-point distances calculated in any available point cloud processing software can provide an initial guide. However, our method leverages the trajectory data for transforming the profiles into the IMU system (see Section 3.4). By doing so, we can analyze all profiles in the same coordinate system and analyze the scanner's co-registration regardless of the direction of travel. In this context, it might be tempting to interpret the transformation parameters between the left and the right scanner as residual mounting parameters (cf. [16]). The problem is that we have to take into account inaccuracies of the trajectory that potentially bias our results (see Section 5.1). In addition, the alignment of the planes is not guaranteed to be ideal (cf. [19]) in the alpine or urban scenes. Therefore, we focus on minimizing errors, regardless of their origin. Figure 19 depicts the transformation parameters between left and right scanners of the already introduced data sets of RW1: D and 402 (cf. Section 5.1.1). Although they were recorded shortly after each other, the results differ significantly for the acquisitions of the two MLS systems. Figure 19: Estimated translation parameters between the left and right scanners for run D (**left**) and 402 (**right**) after transforming all profiles into the IMU system. Figure 18: Results of the estimated transformation parameters (**left**) and standard deviation (**right**) for the alignment of the MLS cloud 802 to the TLS cloud; each value and timestamp refers directly to a profile. For run 402, the translations across the direction of travel (\(t_{x}\) in IMU system) are smaller than those in the direction of travel \(t_{y}\) (Figure 19 right). In contrast, passage D shows deviations across the traveling path up to 12 mm. However, for both systems, \(t_{y}\) shows the largest variance over time. The reason is that RW1 does not contain planar objects with normals oriented in the driving direction that could contribute to the parameter estimation. Conversely, the road surface and the retaining wall provide enough data for the across and height component. The estimated translations \(t_{x}\) and \(t_{z}\) for aligning both point clouds of 402 are consistently below 2.5 mm throughout the entire passage. In contrast, neither component is stable for run D, indicating time-varying systematic influences. The asymmetrical design of the MLS system results in different scan geometries for the two scanners and thus in greater time intervals when scanning the same object compared with symmetrical systems. Especially in the case of poor trajectory quality (e.g., after a tunnel, cf. Figures 9 and 11) the latter effect becomes significant. The good news is that we can compensate for the deviations between the two scanners of run D (see Figure 20). Unfortunately, it is not always possible to minimize the discrepancies between the scanners. For example, for the urban scene I (cf. Section 5.1.3), which was scanned with the same MLS system and processed by the same operator as D, we can improve the alignment, but errors remain in the centimeter range (see Figure 21 left).