# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 5 Results
### Multi-Pass Approach
#### 5.1.2 Rw2  
In addition to the example of a structure directly after a tunnel (RW1), we selected another typical scene in mountainous regions for validating the method. In RW2, we scanned an 18 m high and 200 m long retaining structure that stabilizes a slope adjacent to a rural road and a heavily traveled highway below. The two MLS systems drove the route on the country road on both lanes and in both directions (also in the wrong way: WW) with a traveling speed of approximately 30 km/h. Therefore, employees of the road maintenance department of the Austrian state of Styria temporarily closed the road section during our measurements. We adopt the following notation for point clouds:  
* Pegasus Ultimate:Two: O (1 st lane), P (1 st lane, WW), Q (2 nd lane), R (2 nd lane WW)
* Road-Scanner 4: 802 (1 st lane), 803 (1 st lane, WW), 804 (2 nd lane), 805 (2 nd lane WW)  
Let us first compare two point clouds recorded in the opposite driving direction but on the same lane: 802 vs. 803. The timely gap between the measurements is roughly six minutes. A look at Figure 14 (left) reveals that the two original point clouds do not match perfectly. We observe deviations up to 8 cm on the road surface and up to 5 cm on the retaining structure. It is noteworthy that the magnitude of the deviations varies within the point cloud. Any attempt to align the point clouds by means of rigid body motions will therefore fail, as we demonstrate later.  
For safety-critical applications such as deformation analysis, state-of-the-art georeferencing is proving unsatisfactory, according to our results. Suppose both point clouds stem from two epochs. We would incorrectly assume deformations unless performing a preliminary analysis.  
After estimating parameters and transforming the profiles, we can significantly minimize deviations between 802 and 803 (Figure 14, right). Our idea of correcting overlapping point clouds is essentially similar to the strip adjustment procedure originally introduced for airborne laser scanning data (cf. Section 2).  
Given the eight point clouds (four of each system), we computed the deviations for all \(\binom{8}{2}=28\) point cloud combinations before and after correction. Table 3 displays the deviations' medians as well as their uncertainties.  
The statistics of all 28 combinations show that our method can improve the agreement of MLS point clouds for RW2. Looking at the results of RW2 and RW1 (cf. Table 2), we observe smaller deviations for RW2 in general. The 2.5 km long tunnel before RW1  
Figure 14: Point cloud comparison between 802 and 803 before (**left**) and after (**right**) applying the transformation parameters to the query cloud 803.  
affects the quality of the trajectory significantly. As shown in the upper left and lower right block of Table 3, both systems provide point clouds with similar reproducibility in georeferencing quality.  
It is noteworthy that RW2, unlike RW1, shows an inhomogeneous pattern of deviations (cf. Figures 12 and 14). Major differences between the two scenes are the height of the retaining structures and the measurement duration (apart from the tunnel). It took 14 s to record RW1 and its 11.5 m high retaining structure at 60 km/h. The two MLSs, on the other hand, captured the 18 m high retaining wall (RW2) and the vegetation growing above it in about 34 s.