# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Analysis of Model  
By taking a closer look of the behavior of the model and the features after the encoders, we can gain insights into the theoretical benefit of our model. The goal of this is to provide evidence of the possible superiority of this model compared to others. We first look at the qualitative results of the model iteration process and of our efforts to improve its ability to make estimations over iterations.To further show the behavior of the model over various iterations, we plotted the recall rate against the number of iteration steps for the different positional variables. We show that not all variables improve with increasing iterations, as would be expected of the model. We first looked at the encoded features with an emphasis on the slight changes in the positional variables from the ground truth to see if the features encoded can show human-readable features such as line markings. We were, however, not able to generate useful insights using this methods as the deep encoded features were too far removed from human-readable features. We then also looked at the behavior of the Levenberg-Marguardt optimization algorithm by plotting the delta position for a range of each positional variable to show when the LM algorithm works well and when it does not work well. This proved to provide useful insight, especially regarding which starting position to use and especially why certain starting position strategies proved not to work to generate good results. Furthermore, we illustrated the final behavior of the model using heatmaps to show the inability of the model to find the correct position for various data points. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Sat**} & \multirow{2}{*}{**OSM**} & \multicolumn{8}{c}{**Cross-Area**} \\ \cline{3-10}  & & & \multicolumn{3}{c}{**Lateral**} & \multicolumn{3}{c}{**Longitudinal**} & \multicolumn{3}{c}{**Azimuth**} \\ \cline{3-10}  & & & \(\mathbf{d=1}\) & \(\mathbf{d=3}\) & \(\mathbf{d=5}\) & \(\mathbf{d=1}\) & \(\mathbf{d=3}\) & \(\mathbf{d=5}\) & \(\mathbf{\theta=1}\) & \(\mathbf{\theta=3}\) & \(\mathbf{\theta=5}\) \\ \hline LM [13] & ✓ & ✗ & 27.82 & 59.79 & 72.89 & 5.75 & 16.36 & 26.48 & 18.42 & 49.72 & 71.00 \\ SliceMatch [11] & ✓ & ✗ & 32.43 & 78.98 & 86.44 & 8.30 & 24.48 & 35.57 & 46.82 & 46.82 & 46.82 \\ CCVPE [16] & ✓ & ✗ & 44.06 & 81.72 & 90.23 & **23.08** & **52.85** & **64.81** & **57.72** & **92.34** & **96.19** \\ OrienterNet (a) [12] & ✗ & ✓ & 51.26 & 84.77 & 91.81 & 22.39 & 46.79 & 57.81 & 20.41 & 52.24 & 73.53 \\ \hline AlignNet (b) & ✓ & ✓ & **56.62** & 78.32 & 81.62 & **22.42** & **47.31** & 57.61 & **57.57** & **92.54** & **98.07** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recall rates on KITTI Cross-Area dataset. [ENDFOOTNOTE]
We looked at the behavior of the best-performing model, which utilized the OSM data and started the longitudinal variable at its minimal possible value. Figure 4 shows the success and failure cases of the model. As the loss function utilizes all intermediate positional losses, the model tries to jump close to the ground-truth position as fast as possible. To quantify how much the iterations help, we plotted the recall rate over all 15 steps in Figure 5. As shown, the recall rates of the lateral and longitudinal positional variables improved with increasing iterations. When looking at just the rotational variable, the model shows different behavior. The model, in fact, aims to obtain the correct angle very quickly, before even being close to the correct position. When the model is closer to the correct position, the expected behavior is that the model is better at obtaining the correct angle; however, the opposite is true. Even experiments which only added the azimuth loss for the final iteration did not show that the model improved the azimuth recall rate over the steps it took.