# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
## 3 Results  
In the case of the simulation with a single robot, we present data on the detection accuracy error for the three resolutions in Figure 7a, when the image from a single camera was used. The zero value of the graph represents the real position of the robot, measured by the CoppeliaSim simulation engine, where it is possible to read the absolute position data of every object. As expected, with a smaller resolution of a camera the accuracy drops down. However, the difference between Full HD and HD image resolution is only 4.8 mm on average. In addition, when observing the processing time for an image in Figure 7b the HD resolution may provide twice as fast detection as Full HD. The simulation results with the single robot are presented in Table 1. The data show the relation between the resolution of a camera and its impact on detection accuracy and processing time. The accuracy can be influenced by many aspects, but the processing time of an image is very closely related to the number of pixels. Therefore, in the following real cameras experiment, 1280 \(\times\) 720 px resolution was used when it provides (observing Table 1) good performance between accuracy and processing time. More cameras can detect a gridboard all at once. In this simulation and experiment, we detected it with one, two or three cameras to compare the results. An average value of the position vector for two or three cameras was determined. In Figure 8 there is a comparison of the position accuracy error for \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cameras Resolution (px)** & **Average Accuracy (mm)** & **Processing Time (ms)** \\ \hline
1920 \(\times\) 1080 & 4.1 & 278.9 \\
1280 \(\times\) 720 & 8.9 & 138.5 \\
640 \(\times\) 480 & 117.5 & 51.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average accuracy and processing time of detection in simulation. Figure 7: Simulation results: (**a**) detection accuracy error; (**b**) processing time. single or multiple cameras. As shown in Figure 8b for the experiment, when more cameras were used, the absolute error was not improved significantly; however, the variance of the error was lowered. The reason why the error does not go down substantially when more cameras are used is that at a random position the robot appears in different areas of the images of every camera. In the center of an image, the localization is the most precise, in the sides of an image, the precision of detection is decreasing. This effect is described later in Figure 9.  
The average values of the accuracy error in the simulation with the single robot and experiment presented in Figure 8 are stated in Table 2.  
Figure 9 represents the distortion [35] of nonperfect calibration of a camera. Therefore, more cameras cannot guarantee the absolute accuracy of the detection; on the other hand, the error can be significantly lowered when there two or more cameras are used. In Figure 9, the 14 different positions were measured 40 times by a camera highlighted by the red circle in Figure 5. The set of points A with \(X,Y\) mm was very close to the center of the camera's field of view. The set of points B was the most distant and the dispersion of the 40 measurements is the most significant. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **Average Accuracy of** & **Average Accuracy of** & **Average Accuracy of** \\  & **1 Camera (mm)** & **2 Cameras (mm)** & **3 Cameras (mm)** \\ \hline Simulation & 11.8 & 8.6 & 5.7 \\ Experiment & 18.3 & 15.5 & 12.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of average accuracy error of detection in simulation with the single robot vs. real test. Figure 8: Comparison of detection when one, two or three cameras were used: (a) simulation; (b) experiment. The following graphs are the results of the simulation with multiple robots. In Figure 10, the accuracy of the algorithm is presented, comparing the defined path of the robots and the detected path. The graphs of Figure 10 prove excellent general accuracy of the system.