# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 3 Methodology  
The proposed method aims to process MLS point clouds efficiently to evaluate the geometric quality and to mitigate errors. The main features and prerequisites of our approach are presented below.  
* The input to our method is a reference point cloud represented as a set of points \(R_{i}\leftarrow\mathbf{r}=[r_{x},\,r_{y},\,r_{z}]\) and the MLS point cloud with the timestamp of each measured point as \(M_{j}\leftarrow\mathbf{m}=[m_{x},\,m_{y},\,m_{z},\,m_{t}]\). In this work, we assume that both sets, \(R\) and \(M\), capture stable areas. For preprocessing and identifying such areas (e.g., the road surface), the reader is referred to [34]. The reference point cloud can be the following: (i) The point cloud of a passage acquired in the same direction as \(M\); (ii) The point cloud collected in an opposite driving direction to drive-runs of \(M\); (iii) The data from a second laser scanner, if available; or (iv) A terrestrial laser scanned point cloud.  
As already mentioned, we used commercially available systems with one or more high-quality laser scanners. We evaluated two point clouds from the same scanner in the first two cases. When comparing laser data from different scanners of the same run (iii), differences can occur due to insufficient co-registration and strongly varying trajectory quality, such as next to high structures. Even though there are few up-to-date TLS datasets of the public road network and infrastructure objects, we also apply TLS point clouds as a reference (iv) in our study. The procedure presented below works independently of the choice of reference point cloud.
**Time component**: The MLS quality varies over time. Therefore, the method requires a subdivision of the point cloud into portions with presumably stable quality. The criteria for partitioning depends on the scanner hardware and the expected error level. For example, modern, high-end systems provide profiling frequencies ranging from 150 to 250 Hz [35; 36]. Point sampling rates of up to 1 Mhz are sufficient for precisely estimating the transformation parameters. However, the reliability depends on the scenery. For example, if only the road surface is present, it becomes problematic to find the horizontal alignment reliably.
**Optimization**: We minimize the discrepancy between the two point clouds by optimizing the parameters for aligning the profiles in a least-squares problem. We establish correspondences by searching for the nearest neighbor, but unlike ICP, we optimize them only in the direction of the reference point cloud normals. In this way, we relax the strict point associations and thus make positional uncertainties more forgiving. Eventually, smoothing the obtained transformation parameters guarantees the elimination of remaining outliers.
**Transformation**: With the trajectory data, we can optionally transform point clouds into the IMU system and study the coregistration of multiple scanners.
**Correction**: Finally, we correct the query point cloud profile-wise based on the smoothed transformation parameters. As we show later, we can also transform and match both point clouds in the sense of a strip adjustment.  
Figure 2 gives an overview of the method and the individual steps that are necessary.