# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 5 Results
### Multi-Pass Approach
#### 5.1.1 Rw1
With the estimated transformation parameters, we can correct the query cloud 402 to fit the reference cloud D. We performed a point cloud comparison in commercial software (3 DReshaper17.1.8) before and after correction (see Figure 12). Based on the results, the deviations of about 18 cm on the road surface for the uncorrected case are visible. After correction, 95% of the computed deviations are below 5 cm and the median deviation is 6 mm. Figure 13 shows the distribution of the point deviations before and after the correction. Figure 11: Distribution of point normals of point cloud D (**left**) and the estimated translation in easting (\(t_{x}\)) for point cloud 402 w.r.t. D (**right**). Figure 10: Results of the estimated transformation parameters (**left**) and standard deviation (**right**) for the alignment of 402 to D; each value and timestamp refers directly to a profile. The results so far have focused on the comparison of point clouds D and 402. For the 14 other MLS point cloud combinations, we estimated the transformation parameters in the same way and computed the statistical parameters before and after correction. Table 2 summarizes the results and reads as a matrix. The upper triangular matrix describes the median point cloud distances and the associated variations for all 15 combinations. The robust dispersion is computed via the median absolute deviation, scaled by the inverse of the third quartile of the normal distribution [47]. The lower triangular matrix is shaded green. It lists the same statistical values after applying the correction. We can find the elements \((1,4)\) and \((4,1)\) representing the statistical values for the combination of D and 402 as depicted by the histograms in Figure 13.  
Based on our evaluations (cf. Table 2), we can conclude that our strategy enables us to reduce the deviations between the MLS points clouds significantly. We see median \begin{table}
\begin{tabular}{c|c|c c c c c} \hline  & **D** & **F** & **H** & **402** & **403** & **404** \\ \hline D & - & \(49\pm 11\) & \(107\pm 32\) & \(183\pm 4\) & \(189\pm 9\) & \(193\pm 11\) \\ F & \(2\pm 2\) & - & \(141\pm 61\) & \(135\pm 6\) & \(143\pm 11\) & \(144\pm 14\) \\ H & \(6\pm 7\) & \(9\pm 9\) & - & \(275\pm 19\) & \(288\pm 9\) & \(291\pm 13\) \\
402 & \(6\pm 7\) & \(13\pm 12\) & \(28\pm 13\) & - & \(9\pm 9\) & \(16\pm 11\) \\
403 & \(7\pm 7\) & \(10\pm 9\) & \(9\pm 7\) & \(2\pm 2\) & - & \(7\pm 5\) \\
404 & \(4\pm 5\) & \(11\pm 12\) & \(12\pm 9\) & \(2\pm 2\) & \(2\pm 3\) & - \\ \hline \end{tabular}
\end{table} 
Table 2: Statistics of MLS point cloud distances for all combinations before (not-colored) and after correcting RW1 (green-colored): median \(\pm\) scaled median absolute deviation (MAD) in mm. Figure 12: Point cloud comparison between D and 402 before (**left**) and after (**right**) applying the transformation parameters to the query cloud 402. Figure 13: Distribution of point deviations before and after correcting the query cloud 402.  
deviations of more than 20 cm reduced to 1 cm or less. Interestingly, point clouds 402, 403, and 404 (lower right of the four blocks) match consistently better than point clouds D, F, and H (upper left).