# Automatic Point-Cloud Registration for Quality Control in Building Works
## 4 Conclusions  
This paper presents a new methodology for the automatic acquisition and registration of point clouds for indoor scenarios in construction works. The main contributions of the presented approach are two-fold: (i) the possibility of automatically capturing point clouds based on the Stop&Go method and using an UGV; (ii) a methodology of point cloud registration fully automatic that works with any photogrammetric or laser scanning point clouds. The methodology proposed presents an automatic data acquisition with an UGV equipped with a TLS and an automatic route planner able to calculate the best route and producing no interferences during the construction works. The different point clouds acquired are automatically registered using a combination of detector/descriptor. The integral methodology can be completely autonomous and automatic, with no need of artificial elements that disturb the construction scenario and no need of user interaction. The precision obtained in the automatic registration was evaluated through metric verifications on the resulting point cloud. The errors are below 1 cm in all measurements performed. This precision validates the use of the methodology in quality control scenarios in construction works for the identification and control of construction elements. The methodology presented for point cloud registration consists of three steps: (i) pre-processing of the data filtering and homogenizing of the point clouds, (ii) a coarse registration and (iii) a fine registration. The data were acquired with an autonomous unnamed ground vehicle with the Faro laser scan mounted on it and applying the Stop&Go method. The UGV can operate at night and with no user intervention thanks to the path  
planner. The resultant point cloud has enough quality, density and precision for quality control in building works (less than 1 cm in section dimensions and less than 1.5 cm in wall thickness). For the coarse registration, a combination of Harris 3D detector and PFH descriptor was used, since this combination allows the best detection of feature points on corners, edges and planes. These parametric forms are the most common in building scenarios and provide robust results. The detector/descriptor method allows to be applied on any set of point clouds, captured with any laser scan system, using a UGV or not. In addition, the chosen detector/descriptor-based method uses only geometric information to compute the coarse alignment. This means that it is not necessary to have radiometric information (RGB colour), making the methodology more scalable and suitable for many more study cases. In this line, the Harris 3D/PFH combination was chosen, because it does not need images to perform the calculations, as other detectors and descriptors do, such as SIFT or SURF. Being independent of image capturing makes the methodology scalable an accessible for many more situations. Despite the independence of the laser scan system, the equipment used has some limitations. For example, it has not been designed to be able to climb stairs, although it can go through small bumps such as planks. In addition to the TLS for data acquisition, the UGV is equipped with two laser scanners SICK S300 Expert integrated at the front and the back of the UGV that allow to detect objects and holes. The combination of this system with the path planning makes it totally autonomous. The path planning incorporates an algorithm to recalculate the optimal route in those cases where the target point is inaccessible. This occurs frequently in building construction environments where materials or debris can appear unexpectedly. In order to minimize the traffic of personnel, the data acquisition can be carried out at night, since the laser system is an active system and does not need light to operate. Regarding future works, the first research line will improve the coarse registration for those cases with a considerable number of outliers.