# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 6 Discussion  
Digitization continues to advance with the proliferation of compact and low-cost MLS systems on the market. Today's wide range of products, technologies, and data providers make 3D geospatial data more affordable and accessible. Point clouds are finding their way into many applications. For end users, the challenge is to assess data quality and to eventually decide whether it meets application-specific needs. The paper proposes an efficient solution, focusing on the geometric accuracy of point clouds as a quality parameter.  
Datasheets of MLS systems provide rough estimates of uncertainties under specified scenarios [44, 45]. As a consequence, the quality of the trajectory and thus the MLS point cloud can change significantly over a short period (see example in Section 5.1.2). For this reason, we do not aim to model errors [9, 12] in advance but assess the collected point clouds in post-processing. State-of-the-art methods in quality control evaluate the accuracy of MLS data by reference points, lines, or planes. Their acquisition proves time-consuming, especially for long corridor surveys. The same holds for data processing, where digitizing point and line features [30] or evaluating static laser scans with state-of-the-art software is labor-intensive [26].  
In contrast, our proposed methodology processes MLS data fully automatically. The principal idea is similar to strip adjustment in airborne laser scanning, aiming to reduce discrepancies between multiple MLS passages. The method shifts (e.g., [27]) and rotates the point subsets to achieve coincidence. Scanner hardware and the expected error variation define the partitioning for the point subsets. In the case of profile laser scanners, individual scan lines may be extracted using our method, as long as they provide enough data to estimate the transformations precisely. To assess the reliability of the piecewise transformation parameters, we emphasize inspecting the distribution of point normals. We eventually obtain a smooth transformation function by filtering the parameters over time. By applying the estimated transformation parameters, we are able to minimize deviations between MLS point clouds significantly (cf. Sections 5.1.1-5.1.3 and 5.2).  
The processing pipeline is flexible regarding data input. The MLS point cloud may stem from high-end profile scanners or low-cost 360\({}^{\circ}\) multi-beam scanners. As we demonstrated for the tunnel scenario, the methodology reveals systematic deviations between two MLS passages. When we consider each MLS passage as an independent sample, we can average multiple datasets after having removed the systematic component with our methodology (see Figure 22). By doing so, we achieve a quality-enhanced MLS point cloud,which is  
* More precise and
* More complete, i.e., denser,  
than the original samples. The effort is worthwhile, especially for applications with high-quality requirements such as deformation monitoring.  
One remaining issue is systematic errors in the laser measurements (cf. Sections 5.1.3 and 5.3). There are many influencing factors causing errors and thus distortion of the inner profile geometry. Since our method assumes profile rigidity, minimizing those systematic errors fails. We thus propose to enrich the MLS point clouds with incidence angle and distance information to account for these effects in further analyses (e.g., [42]). For this purpose, we also need high-frequent trajectory data (cf. Section 3.4).