## 5 Instrument Used in Milan and the Specific Mapping Approach Solution Applied to the Case Study  
### Heron(r) Backpack--Mobile Mapping System  
The Heron(r) MS Twin Color mapping system, produced by Gexcel [89], was utilized in the project [90]. The manufacturer offers various versions of the instrument. The main version used in the project (2021-2022 version) includes a smart controller that incorporates the device's intelligence and a compact battery pack. The capture head (Figure 10a) [91]consists of an IMU, two lidar sensors (Velodyne(r) 16-line multibeam) (San Jose, CA, USA) (Figure 10b) [91], and a panoramic camera with two lenses, which can concurrently capture RGB video at 24 Hz with 4K resolution (for point cloud colorization) or high-quality 5K RGB photos upon request (to support 360\({}^{\circ}\) virtual tours). At the conclusion of the survey operations, the acquired data, consisting of LiDAR measurements, IMU measurements, and panoramic images, are processed for individual trajectories. This activity is performed using the Heron Desktop(r) application Version 2.6.0.0 [92] supplied with the instrument, which processes the raw data and applies a patented SLAM algorithm to estimate the instrument's trajectory. The procedure utilizes IMU data to determine the sensor's approximate location and spatial orientation. This is further enhanced by a registration process employing the renowned Iterative Closest Point (ICP) technique to align each point cloud (a singular 360\({}^{\circ}\) scan) with its predecessors. The processing workflow (Figure 11) follows three main steps: odometry, local maps, and global optimization. The odometer step consists of calculating an initial estimate of the trajectory, followed by the instrument during the survey. The initial trajectory estimate is obtained using a geometric SLAM algorithm, and this result is simultaneously compared with the IMU estimate to provide a co-evaluation of both: convergence between the two methods is indicated by green coloring. The second step, local maps, is a way of segmenting the point cloud, previously represented as a continuous acquisition, into data chunks generated by dividing the acquisition trajectory into chunks of defined length. The local map representation creates rigid groups of points that can be aligned during the global optimization phase. \"3D local maps\" subdivide the surveyed point clouds into sectors (Figure 12) similar to static scans acquisitions at a generation distance defined according to the geometries of the surveyed environments. The aim of this final operation is to refine the estimated trajectory, allowing for some flexibility in the alignment of individual data chunks within it to compensate for sensor drift. For surveys that consisted of multiple acquisitions, manual connections between  
Figure 11: Data processing workflow: **(1)** odometer: the LiDAR point clouds are sequentially aligned to compute the geometric SLAM trajectory; **(2)** local map: the trajectory is organized into segments, and the associated point clouds are aggregated into the so-called local maps; **(3)** global optimization: the local maps are interconnected through a bundle adjustment process to minimize residual drifts. Figure 12: The local map phase of the Heron Desktop® software generates local maps, which are presented in various colors. local maps were used to link the roof, exterior, and ground floor, and then the entire network of connections between local maps was optimized. This process reduces the error between local maps through iterations that find the best fit between point clouds. Figure 13 shows views of the 3D network of connections between local maps during the global optimization process. Each colored dot represents a local map, i.e., a point cloud--a segment of the acquisition that can be freely adjusted in position during the optimization process (the dot is placed at the center of mass of the local map). Each blue line represents an iterative best-fit constraint between two overlapping local maps.