# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 7 Discussion and Future Work  
Our results show that URSIM outperforms the state of the art in finding correspondences between a sketch map and a metric map. Some of the best matches returned by URSIM between sketch and metric maps are shown in Figure 15a-c. URSIM is mainly sensitive to the parameter \(m\) representing the threshold to determine when two regions are similar, and this parameter needs to be tuned on a per map basis. However, it should be noted that, in the experiments, \(m\) had values bounded in the lower quarter of all possible values. Figure 14: Sketch map transformed onto the metric map using the affine transformation found by the map matching algorithm of [PERSON] et al. [17]. The two images correspond to the two best results over the sketch map dataset. One can see that having an affine transformation between the map does not lead to accurate map matching when using sketch maps since local scale errors cannot be taken into account. Hence, the transformed sketch map cannot be used for robot navigation or other tasks. (**a**) While the maps are correctly centered, the corridors are not matched together due to the differences in local scaling between the maps. (**b**) Local scale differences make it impossible for the matching algorithm to transform the sketch map onto the metric map. When comparing our results to the correspondences given by a user, our study shows that URSIM has a matching accuracy close to that of a user, with URSIM's \(F_{1}\) scores being 10% under the user's. Our study shows the importance of using the topology and local information around vertices when matching sketch and metric maps. For example, changing URSIM's graph matching method to another graph matching method that does not consider the topology of the environment leads to less accurate matching results. Furthermore, our study shows that the accuracy of the correspondences increases when using the topology and both the position of the anchors in the graph and the standardized sizes of regions to build the region descriptors. However, using the topology and either the position of the anchors in the graph or the standardized sizes of regions did not increase the accuracy compared to using the topology alone. Thus, the position of the anchors in the graph and the standardized sizes of regions are equally important for matching sketch maps to metric maps. Moreover, our study of the matching results of DAM, a state-of-the-art matching method for layout maps [17], shows that metric information is not useful to find correspondences between sketch and metric maps. While layout maps are schematic maps, they are metrically close to correct and clearly represent features of the environment, such as corner and walls. Due to DAM's dependency on finding features and using metric information, it is unable to match sketch maps and metric maps, supporting our assumption that relying on metric information leads to incorrect correspondences. On the other hand, URSIM underperformed compared to DAM when used on partial sensor maps, showing the limitation of our method when dealing with metrically correct maps with partial overlap. Hence, the relevance of the URSIM method is predominantly for highly schematic maps, or maps with a high level of abstraction, such as what could be encountered in a sketch-based interface for robot operators. URSIM depends on finding the most unique regions of the map, called anchors. One subject that remains to be explored is how to find the best possible region attributes to find anchors and create the region descriptors used during the graph matching. Furthermore, future work should look at how to automatically determine the parameter \(m\) using either a learning algorithm or attributes of the sketch map or environment. A future study could also evaluate the usefulness of metric information  
Figure 15: Some of the best matches returned by URSIM between sketch and metric maps, and partial sensor maps.