## 4 Results
### LIDAR and UWB Comparison  
LIDAR and UWB, two positioning systems with similar accuracy designed primarily for indoor environments, were also evaluated. While the static assessment aims to demonstrate the long-term average performance and reveal potential bias, the dynamic part is intended to show performance when applied to a mobile robot and reveal potential short-term error. We assessed the measurement accuracy (static case) and timeliness (dynamic case). The measurements were carried out in a room with dimensions \(4\times 5\) m, equipped with various objects, thus imitating a realistic home environment. The evaluation consisted of two main parts: static (Figures 12-17) and dynamic (Figure 18). Figure 11: Distance distribution histogram for obstacles. Figure 12: Comparison of distance measurements over time; actual distance: 0.5 m; front beacon. Figure 14: Comparison of distance measurements over time; actual distance: 1.0 m; front beacon. Figure 13: Comparison of distance measurements over time; actual distance: 0.5 m; back beacon. Figure 15: Comparison of distance measurements over time; actual distance: 1.0 m; back beacon. In the dynamic scenario, the mobile robot was equipped with a mobile phone and traversed the trajectory through the room with naturally occurring obstacles (furniture, people, and other devices communicating via radio signals, e.g., UWB, Wi-Fi, Bluetooth). Data were recorded every 0.5 m of the mobile platform's distance. The robot moved along a pre-established trajectory at an average velocity of 0.5 m/s. The iPhone was mounted on  
Figure 16: Comparison of distance measurements over time; actual distance: 2.0 m; front beacon. Figure 17: Comparison of distance measurements over time; actual distance: 2.0 m; back beacon. Figure 18: Comparison of distance measurements for dynamic conditions. a mobile robot at a height of 13 cm. In the dynamic scenario, the measurement time was less than 1 s. A car equipped with LIDAR changed its position, and measurements were taken from both the beacons and the LIDAR, which simulated real-world conditions. In this case, to average the results, each measurement was performed five times (Figure 18). The conducted research aimed to assess the accuracy, durability, and integration capabilities of an ultra-wideband (UWB) beacon-based localization system in a varying environment. As part of the experiments, the impact of various factors such as phone position, CameraAssist, line-of-sight conditions (LoS/NLoS), and the presence of obstacles on location accuracy was analyzed. The reference to the UWB measurement method was LIDAR. The analysis of the impact of the phone's distance on the accuracy of the measurements revealed a substantial correlation between the distance separating the smartphone and the beacon and the resultant outcomes, which is also visible in those studies that focused on other environmental options. Differences in phone position along the Z, Y, and X axes caused variability in the measurement accuracy. The Y-axis measurements are less reliable compared to those of the other axes. It can be observed that the average distance values varied depending on the phone's position, which indicates the need to consider this variability when implementing the localization system. Moreover, data suggest that UWB beacon measurements are more accurate and consistent at higher heights (1 m and 2 m) compared to lower heights (0.5 m). The orientation and positioning of the phone's antenna at lower heights might not be optimal, leading to less accurate distance measurements. The analysis of the influence of the CameraAssist feature on measurement accuracy showed that enabling this option worsened the localization precision. This is important as in some situations, e.g., in implementations using iPhone 14 or newer, enabling CameraAssist is necessary. The measurement results in LoS and NLoS conditions showed significant differences in localization accuracy.