# Automatic Point-Cloud Registration for Quality Control in Building Works
## 1 Introduction
In this work, a four-step process is performed: (1) creation of a scan network; (2) coarse alignment; (3) filtering outliers and (4) fine alignment based on the minimum loop expansion (MLE) method. The authors use panoramic images associated to each scan to identify common features between them, so it is not possible to use this methodology for scans without these images or textures. In a similar line, [20] proposed a methodology to organise the scans in a network based on the correspondences detected between pairs of scans. Once the scans are connected, a coarse registration is applied and finally a fine registration performs the alignment process. Although the process is effective, the accuracies achieved are between 9 and 32 mm, values similar to those currently achieved with mobile laser scanner (MLS) systems. MLS allow the generation of 3D models in a unique acquisition, with the direct result of a single point cloud, consequently with one coordinate system [21; 22]. However, the guarantee of metric quality of the 3D models depends on the knowledge of the trajectory of the MLS, which is a complicated task in indoor scenes due to the low coverage of the GNSS signal. Simultaneous localization and mapping (SLAM) techniques are an efficient alternative for the computation of the trajectory but do not guarantee the precision required in applications such as quality control during construction works. The reason is that most MLS systems are equipped with low-cost and lower performance laser devices in order to cope with the weight requirements [23], such as Velodyne HDL-32 or Hokuyo UTM in Zeb-Revo system, that present maximum nominal precision between 2 and 3 cm [24], not \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Target** & **Plane** & **Image** & **Image** & **Image** & **Speed** +** & **Proposed** \\  & **Based** & **[14]** & **[15; 16]** & **Based** & **[17]** & **[18; 19; 20]** & **Method** \\ \hline Target Need & X & & & & & \\ Image Need & & & X & & & \\ Only Pairwise Registration & & & X & X & & \\ Minimum Overlap Required & X & X & X & X & X \\ Noise Sensitive & X & X & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different registration approaches, including our proposed method. comparable with the millimetre-precision of TLS systems [25]. [PERSON] et al. [21] use a MLS to create the 3D models of existing buildings in a short time. However, the errors are between 1-3 cm, which is not acceptable for control tasks in building works, where the maximum deviation allowed for transversal sections in columns [26] is \(\pm\)10 mm. Providing the highest precision of TLS compared to some MLS, TLS has been used for quality control of built environments in repeated occasions, in combination with artificial referencing systems such as artificial targets for the semi-automatic registration of the different point clouds acquired [22]. This requires the intervention of the user in some tasks, especially in the planning of the acquisition and the registration of point clouds into the same coordinate system. Regarding user intervention with MLS, the systems in the market that are adequate for indoor mapping require to be pushed or carried by the user [23]. Thus, their use is limited in scenarios with elements of risk such as obstacles and hanging pieces, as well as in difficult-access areas. This limits their use in most \"under construction\" scenarios. Trying to minimize the user intervention and achieve better results with SLAM techniques and high-quality point clouds, [27] presented a SLAM solution for robotic indoor mapping registration in which static and dynamic scans are used together to achieve optimal results. Static scans provide high quality point clouds, and dynamic scans provide the trajectory and the 3D point clouds of hidden areas between static scans.