## 2 Related Works
### Mapping and Clustering Methods  
Another important research topic in mobile robotics is map creation. In the related literature, two main different frameworks were proposed in order to obtain maps in mobile robotics. First, metric maps, which represent the environment with geometric accuracy. Second, topological maps, which describe the environment as a graph containing a set of locations with the related links among them, with no metric information. Regarding these options, some authors proposed storing information in the map hierarchically, into a set of layers that contain information from the environment with different levels of granularity. Such arrangement permits solving localization efficiently, in two phases. First, a rough, but fast localization is carried out using the high-level layers; second, a fine localization is performed in a smaller area using the low-level layers. Therefore hierarchical maps constitute an efficient alternative to build maps with autonomous mobile robots [41; 42; 43]. Focusing on hierarchical mapping, [PERSON] et al. [44] develop an unsupervised semantic mapping and propose a localization method. SURF points are used to carry out the clustering and the map is corrected by means of odometry. [PERSON] and [PERSON] [45] perform clustering and propose image loop closure with the objective of building hierarchical maps. They work with omnidirectional images and local features. [PERSON] et al. [4] propose an augmented navigation graph, an extreme hierarchical map that consist of 4 layers. On the lowest layer, metric information is stored, so it is easier for the robot to navigate and perform localization tasks. As the level of the layer increases so the abstraction level does, in such a way that the highest-level layer is a graph with a conceptual representation of detected places. Clusters on that layer are connected with an indicator that represents probability of success to make a transition to the adjacent cluster. Additionally, loop closure detection constitutes a crucial step when designing a method to create accurate maps, as shown in [46]. In this work, when a new closure is detected, the new image is stored as a combination of the previous images instead of as a new image. In [47], loop closure detection is performed in two phases using the information of a partially built map: In the first phase, global descriptors are used to find loop closure candidates. In the second one, the loop is closed by choosing the best result among the candidates, using local features. As far as loop closure detection is concerned, the difference of our proposal consists of performing this detection taking advantage of the hierarchical structure of the map. Finally, as stated in the introduction, we propose building hierarchical maps incrementally. Some authors addressed previously this problem by means of incremental clustering. In [12], a method to build topological maps incrementally is proposed, using the SIFT descriptor, and the number of clusters continuously increases while the robot is navigating. The result is a well-separated clustered route, but the number of clusters tends to be relatively high. According to [12], incremental clustering constitutes a good choice when the application meets the following criteria. First of all, when the data cannot be easily represented in an n-dimensional space, but it is possible to calculate similarity measures among individuals. Additionally, if the data computation has a high cost and approximate results may be accepted, incremental clustering is faster, allowing us to perform on-line tasks. Finally, if the number of clusters is unknown, incremental clustering methods set the necessary number of clusters depending on different thresholds. These conditions are met in our work since a similarity measure between global-appearance descriptors can be calculated, the method employed does not need to calculate an affinity matrix, easing online operation and the most suitable number of clusters is not known beforehand.