## 1 Introduction  
The presence of mobile robots in many kinds of environments has increased substantially during the past few years. Robots need a high degree of autonomy to develop their tasks. In the case of autonomous mobile robots, this means that they must be able to localize themselves and to navigate through environments that are a priori unknown. Hence, the robot will have to carry out the mapping task, which consists of obtaining information from the environment and creating a model. Once this task is done, the robot will be able to address the localization task, i.e., estimating its position within the environment with respect to a specific reference system. Vision sensors have been widely used for mapping, navigation, and localization purposes. According to the number of cameras and the field of view, different configurations have been proposed. Some authors (such as [PERSON] et al. [1]) have used monocular configurations. Others proposed stereo cameras by using binocular (such as [PERSON] et al. [2] or [PERSON] et al. [3]) or even trinocular systems (such as [PERSON] et al. [4]). Despite stereo cameras permitting measuring depth from the images, these systems present a limitation related to their field of view. In order to obtain complete information from the environment, several images must be captured. In this respect, omnidirectional cameras constitute a good alternative. They can provide a big amount of information with a field of view of 360 deg. around them, and their cost is relatively low in comparison with other kinds of sensors. Furthermore, omnidirectional vision systems present further advantages. For instance, the features in the images are more stable (because they stay longer as the robot moves), and they permit estimating both the position and the orientation of the robot. Omnidirectional cameras have been successfully used by different authors for mapping and localization [5; 6; 7; 8; 9]. A wide study was carried out by [PERSON] et al. [10], who introduced a state-of-the-art of the most relevant mapping and localization algorithms developed with omnidirectional visual information. An example of a mobile robot that has an omnidirectional camera mounted on it is shown in Figure 1a, and an example of an omnidirectional image is shown in Figure 1b. In the related literature, two main frameworks have been proposed in order to carry out the mapping task: the metric maps, which represent the environment with geometric accuracy; and the topological maps, which describe the environment as a graph containing a set of locations with the related links among them. Regarding the second option, some authors have proposed to arrange the information in the map hierarchically, into a set of layers. The way a robot solves the localization task efficiently in hierarchical maps is as follows: first, a rough, but fast localization is carried out using the high-level layers; second, a fine localization is tackled in a local area using the low-level layers. Therefore, in order to address the mapping and localization issue, hierarchical maps constitute an efficient alternative (like the works [11; 12; 13] show). Visual mapping and localization have been solved mainly by using two main approaches to extract the most relevant information from scenes; either by detection, description, and tracking of some relevant landmarks or working with global appearance algorithms, i.e., building a unique descriptor per image. On the one hand, the methods based on local features consist of extracting some outstanding points from each scene and creating a descriptor for each point, using the information around it (Figure 2a). The most popular description methods used for this purpose are SIFT (Scale-Invariant Feature Transform) [14] and SURF (Speeded-Up Robust Features) [15]. More recently, descriptors such as BRIEF (Binary Robust Independent Elementary Features) [16] or ORB (Oriented FAST and Rotated BRIEF) [17] have been proposed, trying to overcome some drawbacks such as the computational time and invariance against rotation.