# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
###### Abstract  
The increasing demand for 3D geospatial data is driving the development of new products. Laser scanners are becoming more mobile, affordable, and user-friendly. With the increased number of systems and service providers on the market, the scope of mobile laser scanning (MLS) applications has expanded dramatically in recent years. However, quality control measures are not keeping pace with the flood of data. Evaluating MLS surveys of long corridors with control points is expensive and, as a result, is frequently neglected. However, information on data quality is crucial, particularly for safety-critical tasks in infrastructure engineering. In this paper, we propose an efficient method for the quality control of MLS point clouds. Based on point cloud discrepancies, we estimate the transformation parameters profile-wise. The elegance of the approach lies in its ability to detect and correct small, high-frequency errors. To demonstrate its potential, we apply the method to real-world data collected with two high-end, car-mounted MLSs. The field study revealed tremendous systematic variations of two passes following tunnels, varied co-registration quality of two scanners, and local inhomogeneities due to poor positioning quality. In each case, the method succeeds in mitigating errors and thus in enhancing quality. mobile laser scanning; point clouds; quality control; georeferencing; systematic errors; transformation parameters; quality enhancement Article  
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airborne and land-based MLS point clouds based on functional relationships and empirical error values [8; 9]. The drawback of such error prediction models is that they only provide a rough estimate of uncertainty and do not necessarily represent the quality of the data collected. Multipath and outages of global navigation satellite systems (GNSS) distort the vehicle's trajectory. Residual errors in the system calibration or changing parameters due to environmental conditions also systematically influence the 3D point cloud coordinates. Furthermore, laser scanner measurements are prone to systematic variations. Scanning geometry, surface roughness, and reflectivity are all elements to consider [10; 11]. Understanding the systematic errors of individual sensors aids in determining the expected quality of MLS point clouds. However, it is impossible to predict systematic errors and their influence on the accuracy of the generated point cloud. It is also impossible to attribute identified point cloud deviations to actual causes during post-processing. In any case, it is feasible to determine the extent of the errors using reference data. Engineers typically utilize artificial or natural ground control points (GCPs) or establish surface patches with known geometry parameters to validate the absolute accuracy. These methods prove to be time-consuming for long corridor surveys, requiring data acquisition with reference sensors and costly post-processing. MLS systems are on the rise, and recognition among engineers and end users is growing. Its efficient data acquisition makes MLS attractive for many applications. However, quality control of this data is still very costly. Therefore, achieving MLS acceptance will require further efforts in quality control and error reduction, especially for safety-critical applications. This paper aims to contribute to these developments and therefore addresses the following questions:  
* How can we efficiently assess the quality of a commercial MLS dataset? * Which strategy proves most promising for reducing systematic errors in MLS point clouds? We begin with a literature review of related work and introduce the reader to several approaches to modeling, determining, and eliminating errors in MLS point clouds. Based on this foundation, we present our accuracy assessment and error compensation methodology in Section 3. Section 4 gives details on the performed field studies, especially hardware, configuration, and location. We finally present and discuss our results, and conclude our paper with a summary.