## 5 Instrument Used in Milan and the Specific Mapping Approach Solution Applied to the Case Study
### Accuracy of the Survey with iMMS for Geospatial Applications  
It is essential to clearly establish that the level of accuracy required in the project is defined to prevent errors in item recognition and the misallocation of assets to inappropriate  
Figure 14: The Hesai® mid-range lidar PANDAR XT32 [95], as shown before being installed on the wearable mapping device. Currently, Heron® instruments no longer install Velodyne® sensors, as the company has been acquired by Ouster®, and Velodyne products are no longer in production or available on the market (Photo [PERSON]).  
Figure 15: (**a**) The latest version (2024) of the Heron® Twin Color. (**b**) The University of Brescia team during some field tests at one of the Milan locations.  
areas of the building. Essentially, the SLAM survey aims to enable the recognition of the geometries of the various sections or components of a building, with the goal of locating the assets within it. These assets should be accessible through a virtual tour, with transitions between congruent spherical views, and geometrically located within their respective rooms (e.g., the height of a switch relative to the floor level, the width of a door, and the distance between the edge of a window and the adjacent edge of the room). Thus, the virtual tour also provides a global three-dimensional geometric representation, but its accuracy characteristics are not the primary objective. If accuracy were to be the main focus, a rigorous topographical approach would be required, which is unmanageable due to the cost and investment associated with asset mapping projects. In general, the accuracy of a SLAM survey with an asset management objective must first ensure the local congruence of the three-dimensional representation.  
Indeed, if certain areas of buildings are present in three-dimensional views acquired by surveyors at different times or along intersecting survey paths, these detections must be congruent. Therefore, special attention must be paid to the local relative accuracy between different acquisitions, rather than, as is customary in classical topographic surveys, focusing on global topographic accuracy. In fact, the global accuracy of the model for a large area diminishes due to the SLAM tendency to drift along long paths. This is why the final point cloud of the environment (Figure 16) is established through an offline post-processing procedure executed using the Heron Desktop(r) software, which employs a comprehensive SLAM approach. The 3D map is generated by utilizing all the measurements collected throughout the survey.  
This involves a global registration of all the scans, which minimizes misalignment errors among the point clouds and closes any loops in the trajectory. Unfortunately, good global accuracy, as stated before, requires topographic framing operations and time-consuming data processing, which are incompatible with the costs of an expedient survey. For these reasons, the local relative accuracy required between overlapping acquisitions can also be very high, i.e., in the order of 2 to 5 cm, while the global accuracy has been limited to an order of 10 to 20 cm, i.e., the order of magnitude of a SLAM survey in the absence of topographic geometric constraints. Nonetheless, we must remember that the performance  
Figure 16: An example of a 3D point cloud model of a complex of buildings in Milan.  
of the SLAM algorithms can be significantly impacted by the geometric characteristics of the surveillance area and by the presence of closed loops in the surveying trajectories.