# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 6 Conclusion
### Quality of Processed Data  
Data quality was assessed in several ways. First, it was compared with the quality of the reference methods. Table 7 shows the number of points in the point cloud section. The final number of points in the cloud facade on Fleming Square from the computed spherical images is more than 6 times lower than that from the SLAM method. The quality of the facade can also be seen in Figure 11, and significant features were captured. This is also reflected in the quality of the model (Figure 12), where a more significant deformation of the model occurs on higher floors of the building.  
In the case study of Lipova street, the data quality is already comparable to the reference (Table 7), and visibly (Figure 11), the data are better. This may be because all parts of the building were walked \"forward and back\" with the device.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **St. Dev X** & **St. Dev Y** & **St. Dev Z** & **St. Dev XY** & **St. Dev** \\  & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **XYZ [mm]** \\ \hline Fleming Square & 53 & 64 & 55 & 83 & 99 \\ Lipová street & 56 & 61 & - & 83 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Standard deviations of absolute accuracy.  
Figure 10: Ground point deviations: (**a**) case study of Lipová street; (**b**) case study of Fleming Square.  
The point cloud of the facade created from the AI-edited images, using a pre-trained realresnet-x4 plus model, had a very similar accuracy to the facade from the original images. The only significant difference is in the texture of the models. Figure 12 shows the orthophotos of the facade. Both images show the cloud deformation that occurs on the upper floors of the building. More significant deformations are seen in the orthophoto with the AI-modified images. The deformations are very similar in both examples, and only in the model with the original photos are they more blurred. At first glance, the AI orthophoto is significantly sharper and more engaging, especially on the first two floors.  
Figure 11: Quality of point clouds (error scale is the same as in Figure 8; blue color means differences approx. 0–5 cm, and green color means differences approx. 10–20 cm).  
Figure 12: Quality of orthophoto: (**a**) using AI-edited images; (**b**) RAW images; (**c**) details.  
The difference in the quality of the lower and higher floors of the facade is not only due to the higher distortion, which increases with the distance from the center of the fisheye lens, but also because part of the upper floors was partially covered by the GNSS equipment when passing close to the facade.  
The pre-trained ESRGAN model used is not trained to work with spherical images, and therefore, one way to improve AI-edited images and create orthophotos would be to re-train it on its own dataset.  
To prevent deformation of the upper floors, with both types of images, we can choose from several options:  
1. Sensing from a greater distance--this will affect the spatial resolution of the facade.
2. Sensing from the same distance, but the camera and GNSS are placed at a higher elevation on the pole.
3. Setting a more extensive mask when processing the model, so that when passing close to the facade, parts of the image with high distortion are not used.
4. Adding images without using GNSS and without obscuring the top of the images.
5. Combining the above methods.