# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 6 Experiments
### GNSS/SLAM Fusion Framework Experiments
#### 6.4.1 Localization  
To test our new localization module (described in Section 5) we followed different paths through the campus. These routes were recorded in manual driving using a remote control. To test the fusion of the different localization subsystems these paths pass both within a grid map, previously obtained using the architecture shown in Figure 2, and outside it. We obtained this map through the datasets recorded for the experiments of our previous work [42]. Using this map, we are able to locate our vehicle in the environment using AMCL. However, it has been six months since the data was captured. This means that in the map may appear dynamic objects that were in the environment at the time the map was made and that might not be in the environment when the AMCL runs. We can see this map in Figure 11. The paths we followed in the experiments are those shown in Figures 12 and 13. In the path \(R1\) (Figure 12_left_) we circulated entirely within the map. In the path \(R2\) (Figure 12_right_) we circulated in part out of the map, in this way we tested how the system behaves when using  
Figure 11: Grid map obtained in the pre-execution phase (Figure 2), and latter used in the execution phase (Figure 3). We generated this map using datasets previously recorded during the experimental sessions described in [42]. only GNSS. In the path \(R3\) (Figure 13_left_) we circulated approximately the half of the route out of map. The difference with respect to the path \(R2\) is that in this case we went out and entered the map describing a rectilinear trajectory, which can be more favorable to the relocation because AMCL suffers in the turns. The last route was the \(R4\) (Figure 13, right), in which we circulated most of the journey off-map to test the non-divergence of the system. In this route, we found very unfavorable areas for the GNSS because of high trees and nearby buildings. All the paths were done twice, once with GNSS standalone, and another using the GNSS-RTK in floating mode (details about the single-frequency RTK system used can be found in [42]). Figure 12: (**left**) Path \(R1\) around the building located in the upper right quadrant. This path runs entirely through the map shown in Figure 11. (**right**) path \(R2\) around the two buildings on the right side of the image. This path leaves the map in the area of the building in the lower right quadrant of the image. Figure 13: (**left**) path \(R3\) around the two buildings on the right side of the image. This path leaves the map shown in Figure 11 in the area of the building in the lower right quadrant of the image. (**right**) Path \(R4\) around the two buildings on the right side of the image, and the building on the lower left side of the image. This is the path that makes the longest journey off the map. In Figure 14 we can see the trajectories obtained when following the path \(R1\), with GNSS standalone (left), and with GNSS RTK floating (right). This is the only journey that we made entirely within the map. In these conditions, the AMCL localization proved to be very robust. This translates into very small variances what makes the KF output to be very similar to the output of the AMCL. As we can see in Figure 14, the red line (KF) is superimposed on the green line (AMCL), as both provide almost the same location. In both cases, we see that the blue line (GNSS) differs from the fusion, although to a lesser extent in the case of RTK. Figure 15 shows the paths obtained for \(R2\). In this case, the behavior in the map area is the same as shown in the previous case. However, in the area where we travel off-map (\(x>75\) m) the fusion adapts to the observations provided by the GNSS-based subsystem. In this area, when the AMCL uncertainty grows exceeding the thresholds, the Kalman filter output corrects the AMCL state estimation so that when the robot re-enters to the mapped area the AMCL subsystem converges again. Figure 16 illustrates the trajectories made for the path \(R3\).