# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 5 Experimental Evaluation
### Experimental Results and Analysis  
Table 2 presents the accuracy results of hierarchical localization method proposed by [5] on the Aachen and RobotCar Seasons datasets. We use the different combination of image retrieval, feature extraction, and matching strategies to localize a image as illustrated is Section 5.2. As presented in Table 2, we can see that on the Aachen dataset all methods in daytime queries outperform nighttime queries, and NV+SP+SG is the most competitive on all queries. The relatively poor performance in nighttime is mainly because that the nighttime condition suffers more error-prone feature correspondences than the daytime environment. Additionally, the learning-based feature types and matching strategies outperform the traditional methods. Tables 3-5 show the accuracy results of DLALoc with 100 anchors and different RANSAC sampling times using the same combinations of Table 3. Data with improvements compared to Table 3 are highlighted on black bold. The results show that our localization algorithm still maintains the trend on Table 3, confirming that our method can take advantage of the better image retrieval method as well as local feature types and matching strategies. When the RANSAC sampling times is greater than 50 our method can achieve more accurate results at all levels of precision. These improvements are more significant on the nighttime queries. This result further highlights the filtering effect of our method. However, when the RANSAC sampling time is 25, we only achieve a more precise result on the lowest precision level, meaning that the estimated pose is not so accurate that when filtering bad correspondences, some good matching pairs will also be filtered out along with bad ones. Furthermore, when the RANSAC sampling time is 100, the precision improvement is smaller than of the 50 RANSAC sampling time. This indicates that our RANSAC mechanism may converge around 50 sampling times. However, on the RobotCar Seasons dataset, our method performs similar to the Aachen dataset. On dusk and sun sequences, the accuracy tends to saturate. However, our method can achieve a more robust result in the more challenging sequences than all baselines when the RANSAN sampling time is greater than 50. Since the main advantage of DLALoc is that it can filter 2D outliers efficiently, we compare the different 2D feature correspondences of the same query image under baseline method and DLALoc with 100 anchors and 50 RANSAC sampling times on Figure 6. As Figure 6 illustrated, DLALoc does filter out most of the outliers, which is the key to the improvement of localization algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Hierarchical Localization Baseline**} \\ \hline  & \multicolumn{1}{c}{**Aachen**} & \multicolumn{3}{c}{**RobotCar**} \\ \cline{2-6}  & **Day** & **Night** & **Dusk** & **Sun** & **Night** \\
**Distance [m]** & **0.25/0.50/5.0** & **0.5/1.0/5.0** & **0.25/0.50/5.0** & **0.25/0.50/5.0** & **0.25/0.50/5.0** \\
**Orient. [deg]** & **2/5/10** & **2/5/10** & **2/5/10** & **2/5/10** & **2/5/10** \\ \hline NV+SIFT+NN & 82.6/87.3/92.9 & 34.2/46.8/60.1 & 56.7/83.5/94.9 & 46.1/67.8/91.0 & 5.1/10.9/23.9 \\ NV+SIFT+SG & 84.6/88.7/93.7 & 54.5/71.7/86.9 & 63.2/87.9/95.4 & 61.1/74.9/93.5 & 7.2/18.4/33.6 \\ NV+SP+NN & 83.7/88.4/93.2 & 40.8/56.4/73.7 & 59.2/86.7/95.1 & 58.5/69.7/93.3 & 6.7/17.3/31.1 \\ NV+SP+SG & 88.0/94.9/97.8 & 70.2/87.4/98.4 & 67.9/88.9/96.9 & 57.4/67.8/92.6 & 6.4/18.1/30.9 \\ HF-Net+NN & 75.7/88.3/90.1 & 45.9/61.1/78.9 & 55.6/85.6/95.2 & 46.7/74.6/95.9 & 3.2/6.9/17.7 \\ HF-Net+SG & 87.6/92.4/93.7 & 65.4/85.9/94.4 & 64.8/87.1/95.8 & 46.3/67.4/90.9 & 4.7/17.5/24.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Localization results of hierarchical localization baseline.