# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
## 4 Experiments
### Results  
To quantitatively analyze the impact of the scale distributions of landmarks on the recognition performance, we defined nine scale ranges at an interval of roughly \(\sqrt{2}\), as shown in Table 1. For different competing methods, the number of landmarks falling into respective scale ranges was recorded, and their landmark scale probability was subsequently generated to examine landmark scale distributions.  
Figure 4 shows the precision-recall curves of two competing methods (Edge-Boxed and SP-Grid) and our MSW approach on the four datasets, and Figure 5 shows the scale distributions of landmarks generated by three methods on all the datasets. In all four datasets, our method significantly outperforms the competing methods and this superior performance is consistent regardless of whether the appearance changes between repeating visits are due to viewpoint (Figure 4a,b) or illumination (Figure 4c,d).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Index** & **Scale Range** \\ \hline
1 & [0, 0.02) \\
2 & [0.02, 0.05) \\
3 & [0.05, 0.09) \\
4 & [0.09, 0.14) \\
5 & [0.14, 0.23) \\
6 & [0.23, 0.34) \\
7 & [0.34, 0.48) \\
8 & [0.48, 0.70) \\
9 & [0.70, 1] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nine indices with the corresponding scale range.  
Figure 4: Performance of MSW, SP-Grid and Edge Boxes on: (**a**) Mapillary Berlin; (**b**) Gardens Point Campus; (**c**) Freiburg Across Seasons; and (**d**) Oxford RobotCar dataset.  
Figure 5: Landmark distribution of MSW, SP-Grid and Edge Boxes on all four datasets. Edge Boxes tends to produce a high number of small landmarks and the scale distribution is non-uniform. SP-Grid produces a peaked landmark scale distribution at close to scale index 4. Scale distribution of our MSW method is uniform.  
We attribute this improvement in performance to the difference in the distribution of the landmark scale (size), as depicted in Figure 5. Edge Boxes tends to produce a high number of small landmarks and the scale distribution is non-uniform. SP-Grid produces a peaked landmark scale distribution at close to scale index 4. As discussed below, a large number of small landmarks contributes negatively toward matching accuracy of Edge Boxes, and a peaked distribution with SP-Grid reduces its ability to handle viewpoint changes when matching landmarks have different scales.  
Figure 6 provides qualitative results of the three competing methods in matching one pair of images from the Gardens Point Campus dataset, with Edge Boxes (Figure 6a), SP-Grid (Figure 6b) and our MSW method (Figure 6c), where bounding boxes (landmarks) of the same color are considered to be matching. Both Edge Boxes and SP-Grid produced a false positive result, whereas our method produced a correct true positive result. The failure of Edge Boxes is caused by the small objects, whereas that of SP-Grid is caused by the peaked distribution of landmark (bounding box) scale.  
Finally, we compared the proposed method with the state-of-the-arts using average precision (AP). As presented in Table 2, our method quantitatively beats Edge Boxes and SP-Grid method, and this result is consistent with those provided above. Besides, the proposed method significantly outperforms the NetVLAD, a deep model based approach using more compact representation on Gardens Point Campus dataset, and our MSW method achieves comparable performance on the other three datasets. This demonstrates the effectiveness of our method.  
Figure 6: An example illustrating the pairwise matching results achieved by: (**a**) Edge Boxes; (**b**); SP-Grid; and (**c**) our MSW method. Both Edge Boxes and SP-Grid produced a false positive result, whereas our method produced a correct true positive result. It is shown that the failure of Edge Boxes is caused by the small objects, whereas that of SP-Grid is caused by the peaked distribution of landmark scale. Our method leads to true positive matches by utilizing the landmarks of appropriate distribution and scales.