# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 4 Discussion and Conclusions  
The results of this paper indicate that a mapping device made from a commercially available action camera and low-cost GNSS equipment is a suitable tool for mapping outdoor areas, of course with a given degree of accuracy. However, for this purpose, accurate synchronization of the spherical camera and GNSS clocks is required. For this purpose, the 360 VisualSync software was developed and tested together with the constructed device. The first test was carried out on a square with trees and surrounded by urban buildings. Measurements were compared with the SLAM method using the Faro Orbis device. The spatial accuracy of the low-cost method, determined by comparing point clouds here, was 47 mm. The absolute spatial accuracy was then evaluated using ground GNSS RTK points, and, in this case, a spatial standard deviation value of 99 mm was found. The second test site was on Lipova street in KopidIno. Here, a housing development with detached houses was surveyed. The point cloud was first compared with the point  
Figure 13: Facade point cloud quality (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). cloud calculated from the images from the Mavic 3E drone. Although the plane's standard deviation was very similar to the previous test, in this case, the spatial standard deviation of the difference between the point clouds was significantly higher, at 82 mm. This may have been due to the accuracy of the reference point cloud. The absolute reference points used here were independently determined by the Cadastral Office. The standard deviation of this comparison was identical to the first test, at 83 mm. Compared to the work of [13], where the absolute 2D standard deviation achieved was between 17 and 20 cm, the precision achieved in this article is 2 times higher. Finally, the products were evaluated in terms of data quality. In general, in photogrammetry, a common problem is the evaluation of simple single-color objects. This was also reflected in this case, especially for the facade from the first test, where only distinctive elements, such as windows, were calculated with sufficient confidence. The point cloud from the second testing is of significantly higher quality. When measuring this area, each section was walked \"back and forth\". Following the results of the data quality of the house facade capture, several procedures were proposed to improve the quality of the measurements. These suggested approaches were then verified in an experiment. The results from this experiment show an improvement in the quality and density of the facade point cloud. Unfortunately, as in the previous case, there is a noticeable deformation in the higher parts of the facade. The processing of spherical images with the Real-ESRGAN machine learning tool, designed to increase the resolution of the images, did not produce significant changes in the accuracy of the point cloud or its density. The texture quality of the model and orthophoto are greatly affected by the modified images, with results that are sharper and of higher visual quality than when processed with the original images. Using these edited images also makes deformations in the orthophoto, which are otherwise hidden behind the lower quality of the captured data, more noticeable. The quality of the resulting point clouds proved to be highly dependent on several parameters: the distance between the camera and the object (typically 3-10 m), the interval of frame extraction (1 s), the operator's walking speed (around 4-5 km/h), and the GNSS signal quality. These factors directly influenced the density, sharpness, and geometric consistency of the reconstructed point cloud. Sharp object edges, repetitive textures, and strong contrast improved the reliability of point matching, whereas uniform or single-color surfaces often led to missing areas or deformations.