## 2 State-of-the-Art of Systems for Indoor Mobile Mapping (iMMS)
Consequently, if it is necessary for engineering reasons to extract a BIM model of a site, it is necessary to run a TLS survey, resulting in the incurred costs of this operation. In the circumstance that the BIM model is only functional for facilitating the implementation of digital procedures to improve the efficiency of building management procedures, as in our scenario, these expenses are totally unrealistic. The introduction of mobile surveying technologies has been a significant contributor to the reduction of surveying costs in this context. It was around the year 2000 that the scientific community became interested in the development of mobile mapping systems (MMS) [33; 34; 35]. The market saw the emergence of surveying systems based on mobile mapping technology between the years 2000 and 2005. These instruments were equipped with a LiDAR sensor, a camera, an inertial IMU system, a DMI, and GNSS receivers [36]. Outdoor mobile mapping systems can efficiently map extensive open areas, such as metropolitan centers and infrastructure. Nevertheless, outdoor mobile mapping systems have not influenced the construction and civil engineering sectors, as their operation relies on determining the instrument's position using the GNSS approach, either in real time kinematic (RTK) mode or through post-processing. However, in buildings and residential construction, GNSS signals are often untrackable. This issue has typically restricted the use of outdoor mobile mapping instruments in these environments [37; 38; 39; 40]. Since the 1990s, researchers, particularly in the field of robotics, have been developing and testing algorithms for self-localizing robots in confined environments, such as warehouses. Such an algorithm is known as SLAM (Simultaneous Localization and Mapping), a concept proposed and defined by [PERSON] in 1986 [41], which permits an instrument equipped with appropriate sensors to autonomously determine its position within a building or a three-dimensional environment (initially applications involved automatic positioning within automated warehouses) simply by observing its own position with respect to a 3D panorama of fixed elements in space, such as walls, floors, and pillars [42; 43; 44]. By analyzing the changes in its position relative to a static environment, the instrument applies SLAM algorithms to estimate its trajectory and achieve self-localization within that environment. Consequently, the device can indirectly survey the 3D geometry of the surroundings. This solution allows operation in environments where GNSS signals are not receivable and can therefore be employed indoors, hence the designation of indoor mobile mapping systems (iMMS). The SLAM positioning and 3D survey were initially introduced in robotics, allowing a robot to estimate its own position and orientation in space, thanks to measurements of multi-beam Lidar and IMU sensors, starting from an unknown location [45]. Commercial SLAM solutions for professional operators began to emerge in the 2010s, and these devicesprimarily started using multi-beam LiDAR sensors from the automotive sector, coupled with IMU sensors of variable accuracy [46; 47]. At the moment, SLAM-based iMMS offers efficient indoor surveys within buildings, urban areas, tunnels, and underground mines, with extremely fast timelines and local accuracies in the range of approximately 5 mm/2 cm. There are also numerous applications in environmental, geospatial, cultural heritage, and even BIM modeling [48; 49]. In fact, it is possible to utilize the point cloud generated by these systems for BIM modeling [50], which is used for BIM with a Level of Development (LoD) classification compatible with the accuracies of the iMMS. The main limitation of such solutions lies primarily in local accuracy and in the geometric drift that affects the SLAM algorithm, in particular in medium- to large-scale surveys.