## 4 Conclusions  
In order to examine the performance and limitations of different indoor mobile mapping systems (IMMSs), knowledge of the basic principles of measurement of this advanced surveying methodology (Introduction, Section 1) has been reviewed. IMMSs represent one of the most promising areas of geomatics for their outstanding capability to acquire the coordinates of millions of points in a very short time, by _simply walking_ in the area of interest. Figure 23: Comparison between the datasets under examination and a meshed reference. On the right are the histograms of the signed distances. C2M: cloud to mesh. \begin{table}
\begin{tabular}{c c c c} \hline  & **Kaarta Stencil** & **Leica Pegasus Backpack** & **GeoSlam Zeb-Revo** \\ \hline Acquired points Path 1 & 30,000,000 & 58,729,331 & 7,024,678 \\ Acquired points Path 2 & 13,588,000 & 99,518,813 & 8,592,333 \\ Data completeness \({}^{*}\) & 1 & 3 & 2 \\ Feature recognizability \({}^{*}\) & 3 & 2 & 1 \\ Visualization & 2 & 3 & 1 \\ effectiveness \({}^{*}\) & 3 & 1 & 3 \\ Wrong double surfaces \({}^{*}\) & 3 & 1 & 3 \\ \hline \end{tabular} \({}^{*}\) Subjective evaluation—average value as assessed by five operators (1 = worst; 3 = best).  
\end{table} 
Table 3: Subjective evaluation of qualitative criteria considered in the test. Figure 24: Leica Pegasus Backpack, detail of a horizontal slice of data shown in Figure 23 (reference data in black, compared data in colour). Nevertheless, the complex analytical model is implemented by different algorithms (Material and Methods, Section 2.1) and it can be considered as a two-step process. Firstly, there is multi-sensor _mapping_, where instrumental hardware efficiency plays a key role, followed by a refinement, generally called SLAM. This is not really simultaneous process, and thus it is impossible to predict the final correctness and accuracy obtainable from a given IMMS. Besides, it is difficult to compare different systems, also considering that hardware/software configurations can dramatically change from one to another and, above all, that the final results are strongly influenced by the adeptness of the software. A comparative procedure is proposed here with the paper considering the point clouds obtained by the Kaarta Stencil, Leica Pegasus Backpack, and GeoSlam Zeb-Revo systems (Material and Methods, Section 2.2). The test field (Material and Methods, Section 2.3) was chosen to as far as possible reproduce real, variable conditions; therefore, a closed and an open footpath were defined, with both considering internal and external environments. The test area was previously measured by means of static scans with a Z+F TLS, which were assumed to represent the \"ground truth\" (Material and Methods, Section 2.4). The three IMMS clouds were then aligned in the same reference frame as the ground truth. After that, quantitative criteria were fixed for the evaluation (Material and Methods, Section 2.5): cloud to cloud, point to point, and cloud to feature distances were assessed. Finally, qualitative criteria (Material and Methods, Section 2.6) were defined for additional evaluations. A large part of the paper is devoted to describing data acquisition (sometimes with operating problems) and to discussing the test results for each system (Results, Sections 3.1-3.3), with quantitative evaluations (Results, Section 3.4) of the differences between the datasets under examination and the reference cloud. To summarize the results provided by the tests, the Stencil and Pegasus Backpack data present distances of less than 8 cm with respect to the reference, while the Zeb-Revo data present smaller discrepancies. For all the systems, these mean distances are quite similar for datasets acquired by following the closed or the open path. The qualitative evaluation (Results, Section 3.4) concerns the completeness of the data, modelling of the artefacts, and level of detail of the three clouds enabling the targets, spheres and architectural details to be recognized (or not).