## 3 System Tests
### Test in the Laboratory  
The first stage relates to testing the position accuracy from the vehicle to the desired position on the map. Figure 7 shows a vehicle equipped with a vision system and few ARTags located on the test track in the laboratory. Tests on the correctness of estimating the closest distance to the indicated target were carried out in good lighting conditions, thanks to which the differences between the actual distance and the one determined by the algorithm were small, with the average distance of up to 3 m, the relative errors were in most cases smaller than 1% as shown in Figure 8.  
Figure 6: Determination scheme of position correction vector when three markers are detected. Figure 7: General overview of laboratory test environment. For worse conditions, for example underexposure, the maximum detection distance was significantly reduced. The strong light falling on the marker could even prevent detection. A detailed description was presented in [21] where it was noted that absolute measurement errors increase with the distance. The most accurate measurements were obtained for distances up to 3 m and this corresponds to the distance for which the camera was calibrated. In the test conducted in the laboratory, there were five marker destinations that the robot had to travel to. In every test, the robot made 76 drives on a randomly selected point and stopped there. Exactly 12 ARTags were placed on the test track. Each ARTag was 180 mm \(\times\) 180 mm in size. During one test, the robot covered about 668 m. The first test was an autonomous drive with the vision system turned off. This means that only wheel odometry was taken into account with some undesirable drift coming from the inertial sensor. We can see driving accuracy results marked by the letter (a) in Figures 8-12. In the figures we marked by the letter (c), the position deviations in the axis \(x\) and \(y\) with cameras turned off. The exact same sequence was reproduced in the second experiment with the vision system and odometry turned on (every letter (b) in the figures). Deviations in this case are presented in figures (d). The system had two cameras on board. Figure 13 presents differences between set and reached positions in the 76 driving experiments. Figure 13a is a graph representing position accuracy in the \(x\) direction with cameras turned off and Figure 13c with cameras on. Comparing Figure 13b,d, which represent the position deviations, we can see that the case with cameras on the rover increased its position accuracy. Figure 13e,g represent the position accuracy in the \(y\) direction. When cameras were turned on, the accuracy was better, this is shown in Figure 13f,h. Figure 13i,j show, respectively, the distance deviation when the cameras are turned off and on. Those two show that there was a significant improvement in the accuracy in determining the rover position when we applied vision system distance calculation to the wheel odometry. Table 1 shows the result of quantifying the positioning accuracy without cameras and with them. The sum of the deviation modules, measured in the individual axes and the sum of the distance deviations were used as the indicator. It should be noted that the vision system increased vehicle position accuracy by about 50 percent. Figure 8: Accuracy measurement of the first control point. (**a**) Green points represent measured positions when the robot was standing still. The red point is an aim point. In this experiment, the robot had the vision system turned off. (**b**) The robot had the vision system turned on. (**c**) Deviations from the aim point in \(x\) and \(y\), the vision system was off. (**d**) Deviations from the point where vision system was on. Figure 10: Accuracy measurement of point 3. (**a**) Green points represent measured positions when the robot stops. The red point is an aim point. In this experiment, the robot had three vision system turned off. (**b**) The robot had the vision system turned on.