# Automatic Point-Cloud Registration for Quality Control in Building Works
## 1 Introduction
This technique allows us to generate high-quality point clouds in an automatic way using SLAM algorithms, but the global accuracy of the process is between 25 and 50 mm, exceeding the limits allowed for quality control of works in construction [26]. This global accuracy is formed by around 3 mm error from the nominal accuracy of the LiDAR used (SICK 2D laser scanner), and consequently, it can be deduced that more than a 20 mm error is introduced by the data processing. Thus, the generation of quality and complete 3D point clouds from building interiors requires the use of TLS because of their high precision and the dynamic performance of the measurements in order to cover all the scenes. However, the methodology that exploits the advantages of this combination has not been established yet. For this reason, this paper is focused on two main goals: (i) minimizing the user interaction during the infield works and the processing tasks and (ii) establishing an automatic workflow to register all the scans without any target on the scene and without user intervention. Taking this into account, this paper presents a new methodology for the automatic registration of point clouds acquired with an unmanned ground vehicle (UGV). Nevertheless, this approach is applicable to point clouds coming from other photogrammetric or laser scanning sensors. The results are subjected to their use in the quality control of the different phases of a construction work. Regarding point cloud registration, different approaches have been developed in the scientific community. The pre-alignment of point clouds is usually performed based on detectors and descriptors, that is, algorithms that detect points of interest on 3D and describe them according to their geometric or radiometric characteristics. There are several 3D detectors and descriptors, focusing on different special characteristics [28; 29]. Thus, the optimal detectors and descriptors depend on the particularities of each case [30], and their selection affects the result. The detector scale invariant feature transform (SIFT) [31] has been widely used in automatic registration tasks, since it is not affected by the scale. Although it was initially developed to work with 2D information from images, it was adapted by [32] to operate in 3D using the principal curvature of the points in the role of the intensity of the pixels. [33] used the SIFT detector to automatically register 3D point clouds without artificial targets, but the test was performed in a single building without surroundings. [34] developed a methodology in which the 3D point cloud and the associated RGB /intensity images were used together to compute the registration. Their method was highly sensitive to the size of the overlapping area and depended on the acquisition of images associated to the scans. Moreover, the SIFT detector requires a lot of time to complete the processing, and its performance is optimal for the registration of two point clouds [30] but does not perform correctly for registering a higher number of point clouds, as is the case in this study. Similar results are obtained with the normal aligned radial feature (NARF) detector, which takes advantage of the key points in the borders and significant geometric structures [30]. The set of resultant key points is poor, so it works for the alignment of pairs of point clouds individually. The Harris 3D detector detects corners and edges through the analysis of the relation of each point with its neighbours. A set of point rings is generated for each group of points, and their centroid is determined. A paraboloid is adjusted to the neighbour points in each centroid, and PCA is applied to compute the mean square surface of the paraboloid. Finally, the evaluation of the changes in the surface is performed through the analysis of the derivatives of the surface, in combination with a continuous Gaussian function [35; 36]. The Harris 3D detector is invariant to rotation, scale, variations in luminosity and presence of noise.