# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 5 Discussion and Conclusions  
In this paper, we introduced AlignNet, a cross-view outdoor localization method for augmented reality that is based on fusing map and satellite data. The model is an extension of the previous LM model and improves it in such a way as to be competitive on the task of small-scale localization on the KITTI dataset. For this work, we introduced the fusion of satellite and OSM data and changed the architecture and training procedure to beat the state of the art's lateral recall rate within 1 m and azimuth recall rate at 3 and 5 degrees. We analyzed the behavior of our best-performing model and conducted an ablation study to show the benefits and shortcomings of the model. When analyzing our best-performing model, we found that the model is able to align the positional variables over its steps up to a certain point but still relies heavily on a good initial guess at the first step the model makes. This suggests that the model in its current form does not yet take full advantage of its iterative nature but does point towards sample-wise optimization as a viable path of research going forward. Furthermore, we showed that OSM data is highly beneficial in the task of small-scale visual localization. For the lateral positional variable, we show that the recall rate within 1 m receives a boost of up to 18%. In the end, we have shown that our sample-wise optimization strategy is a viable alternative to other methods and have shown a promising direction for future research in the field of visual localization. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Name**} & \multirow{2}{*}{**Sat**} & \multirow{2}{*}{**OSM**} & \multirow{2}{*}{**Start**} & \multicolumn{6}{c}{**Cross-Area**} \\ \cline{5-12}  & & & & \multicolumn{4}{c}{**Lateral**} & \multicolumn{4}{c}{**Longitudinal**} & \multicolumn{4}{c}{**Azimuth**} \\ \cline{5-12}  & & & & \multicolumn{4}{c}{**Lateral**} & \multicolumn{4}{c}{**Longitudinal**} & \multicolumn{4}{c}{**Azimuth**} \\ \cline{5-12}  & & & & \multicolumn{4}{c}{**d = 1**} & **d = 3** & **d = 5** & **d = 1** & **d = 3** & **d = 5** & **\(\theta\) = 1** & **\(\theta\) = 3** & **\(\theta\) = 5** \\ \hline \multirow{5}{*}{Ours (a)} & ✓ & ✓ & _lon - 1_ & 56.62 & 78.32 & 81.62 & 22.42 & 47.31 & 57.61 & 57.57 & 92.54 & 98.97 \\ \cline{2-12}  & ✓ & only nodes & _lon - 1_ & 17.50 & 37.39 & 46.43 & 9.10 & 24.26 & 34.83 & 27.41 & 71.88 & 96.67 \\  & ✓ & only lines & _lon - 1_ & 58.71 & 79.66 & 83.49 & 19.89 & 45.32 & 55.33 & 57.96 & 92.85 & 99.00 \\  & ✓ & only areas & _lon - 1_ & 15.79 & 35.71 & 45.17 & 9.25 & 24.53 & 35.04 & 26.80 & 72.57 & 96.83 \\  & ✓ & ✗ & _lon - 1_ & 17.89 & 38.12 & 46.80 & 8.91 & 24.16 & 34.78 & 27.31 & 72.02 & 96.65 \\ \hline \multirow{5}{*}{AlignNet (b)} & ✓ & ✗ & center & 42.96 & 73.97 & 81.20 & 12.56 & 32.47 & 43.07 & 88.29 & **96.90** & **99.67** \\  & ✓ & ✗ & _lon - 1_ & 40.71 & 69.54 & 76.08 & 5.30 & 15.08 & 23.15 & 58.13 & 92.95 & 97.71 \\ \cline{1-1}  & ✓ & ✓ & center & 50.13 & 78.61 & 84.01 & 7.12 & 20.21 & 31.66 & 49.70 & 78.71 & 87.58 \\ \cline{1-1}  & ✗ & ✓ & center & 52.82 & 80.52 & 85.11 & 10.30 & 28.48 & 42.35 & 44.84 & 75.44 & 85.93 \\ \cline{1-1}  & ✗ & ✓ & _lon - 1_ & 88.82 & 81.08 & 84.46 & 14.88 & 36.77 & 49.18 & 49.87 & 77.26 & 86.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: **Recall rates on KITTI Cross-Area dataset. Best, second-best, and third-best results are marked. (a,b) Base model with both satellite and OSM data with the _lon - 1_ starting position. The model is compared to the same model but with a certain modalities removed. When removing the lines modality, the results greatly suffer, while only keeping the satellite and line modalities keeps the accuracy. **