# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 3 Obtained Results  
### D Models of the Artificial Tunnel Segment Comparison  
The TLS survey, operated by a RIEGL LMS-Z420i, was subdivided into six scan positions evenly distributed along the midline of the tunnel section to maximize the visibility of the surveyed area. In the post-processing phase, each scan was co-registered and rototranslated based on cylindrical targets [18]. Then, the whole point cloud (83,932,625 points) was registered, in Microstation Connect Edition, to the visible targets among the 56 available (Table 2). Each scan was fitted with a multi-station algorithm to improve surface coplanarity in overlapping areas by introducing a scale factor based on the cloning effect of homologous geometries. The overall procedure accuracy results in an RMS of 0.025 m,  
Figure 4: Scheme of the cave survey. following the target survey, confirming its reliability as a benchmark for the next phase of the investigation. Consequent to the TLS survey, PLS and SfM ones were carried out on the same test site. The PLS survey was performed with a Kaarta Stencil 2-16 portable laser scanner, based on the SLAM algorithm [65]. The platform integrates a Velodyne VLP-16 LiDAR system composed of 16 rotating profilers (each with a different orientation with a Field of View (FOV) equal to 360\({}^{\circ}\)Hx30\({}^{\circ}\)V), a low-cost IMU, and an optical sensor. The Stencil 2 is based on the Visual-LiDAR Odometry and Mapping approach [66]. The main workflow provides, firstly, a visual-inertial odometry algorithm, performing a frame to frame motion estimation, which combines each future to the depth data derived by the LiDAR and optimizes the solution considering the IMU information; then, the LiDAR odometry algorithms exploit speed data measured by the odometer for the point cloud registration. Finally, the identification and matching of geometric features in the point cloud allow optimizing the registration, using the previously estimated pose constraints. Thanks to this approach, Stencil 2 can generate a point cloud of the surveyed environment, registered in a local system, in real-time. Data acquisition was performed following a straight path, which, like SfM, was covered back and forth, adding segments at the beginning and end of the test section to survey its terminal portions better. The raw data acquired were firstly processed using the Adaptive Data Reply tool, provided by Stencil 2. The tool allows to simulate the scanning at a lower speed and possibly change several parameters to optimize the scan matching process. The resulting point cloud was roto-translated in the same reference system of the TLS one by employing the previously surveyed reference targets, and software obtaining the residuals showed in Table 3; the quality of the results allowed to proceed comparison with the reference cloud. The SfM was performed by acquiring a sequence of images with a Canon EOS 100D camera along a squiggly path, back and forth, in the tunnel's main direction. The position of each capture and the path were selected arbitrarily during the acquisition. The whole set of 245 images was then imported in Agisoft Metashape to reconstruct the point cloud by the employment of the SfM algorithm. Preliminary, the coordinates of the targets were imported. Their position was collimated on images to co-register the resulting point cloud (Table 4) to the TLS one and allow the comparison. The whole process took approximately 90 min of calculation time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scanposition** & **Tie Points** & **RMS 3D [m]** & **Distance [m]** \\ \hline
1 & 8 & 0.007 & - \\ \hline
2 & 6 & 0.016 & 19.36 \\ \hline
3 & 8 & 0.011 & 17.33 \\ \hline
4 & 4 & 0.010 & 23.68 \\ \hline
5 & 6 & 0.036 & 14.90 \\ \hline
6 & 8 & 0.007 & 23.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TLS scans precision. \begin{table}
\begin{tabular}{c c c} \hline \hline
**RMSE X (m)** & **RMSE Y (m)** & **RMSE Z (m)** \\ \hline
0.0195 & 0.0063 & 0.0031 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PLS co-registering errors.