# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 5 Results
### Multi-Pass Approach
#### 5.1.1 Rw1  
Since construction work took place at the highway near RW1, we drove at 60 km/h and 80 km/h in a convoy. An escort vehicle of the Austrian Highway Agency (ASFiNAG) drove behind the MLS systems for safety reasons. Within a few seconds, the two MLS systems passed by RW1 one after the other, a total of three times. Although both MLS systems produce a point cloud for each scanner, we devote this section to analyzing the point clouds from the right scanner. To refer to the individual point clouds, we use the following notation:  
- Pegasus Ultimate:Two: D (60 km/h), F (80 km/h), H (80 km/h)  
- Road-Scanner 4: 402 (60 km/h), 403 (80 km/h), 404 (80 km/h)  
With these six point clouds, there exist \(\binom{6}{2}=15\) point cloud combinations. It is possible to compare between the systems (e.g., D vs. 402), between the epochs (e.g., D vs. F), and between both systems and epochs (e.g., D vs. 403). Let us start by comparing point clouds D vs. 402. Figure 10 depicts the estimated transformation parameters in the left column and the associated precision in the right column. We separate these two because the range of transformation parameters is large compared with their calculated standard deviation. Each point in the time series represents the results for an extracted profile of the MLS point cloud. When looking at the graphs, one can make the following observations:  
**Filtering and noise:**: It becomes evident that the filtering process is necessary to obtain smooth transformation parameters over time or profiles. The filter width of the moving average in this example is 50. Interestingly, the noise of the translations (\(t_{x}\), \(t_{y}\), \(t_{z}\)) and the rotation angle \(\theta\) are larger than the standard deviations would suggest. This fact confirms our statement that we cannot fully rely on the estimated precision as a quality measure. **Local artifacts:**: Towards the end (i.e., at time 08:28:22), \(t_{z}\) and \(\theta\) and their standard deviation deviate strongly from the rest of the time series. This artifact is caused by poor distribution of point normals (see the next point) and a small number of points. The peak in \(t_{z}\) and \(\theta\) at 08:28:12 occurs exactly where the construction vehicle is parked on the first lane of the highway. The distance between the nearest neighbors on the highway represents the actual displacement in \(z\). In the case of complex objects such as vehicles, the nearest neighbor query might bias the parameter estimation. **Point number and distribution of point normals:** The smallest variations occur in the height translation \(t_{z}\) (third row, left in Figure 10). If we plot the distribution of point normals (cf. Section 3.6) along all profiles (see Figure 11 left), we see that bin 51 dominates throughout the profile-tagged MLS point cloud. The normals associated with bin 51 point upwards (cf. Figure 7), suggesting that most points lie on the road surface. Hence, the geometry and the number of points prove to be quantitative quality measures. We can also identify the retaining wall and observe how the presence of bin 29 decreases as the vehicle travels along the highway. Towards the end of the trajectory, the number of points is small, which significantly affects the uncertainty of the parameters \(t_{z}\) and \(\theta\). **Horizontal translation:**: The highway runs roughly northeast to southwest, so the points on the retaining wall contribute equally to the east (\(t_{x}\)) and north (\(t_{y}\)) displacement calculations. In Figure 11 right, we plotted the estimated translation \(t_{x}\) as a color-coded point cloud. It is noticeable that the separation plot in Figure 9 shows similar deviations. Despite the separation being a good first indicator of the expected quality of the MLS point clouds, the similarities are rather coincidental. The position and attitude separation describe the differences in the forward and backward computation of the error-prone trajectory, while we compare the already georeferenced point clouds, which are also affected by errors.