## 2 Related Works  
### Image Description  
As explained in the introduction, mobile robots have to solve the mapping and localization problems during their autonomous navigation. To deal with these tasks the robot has to capture relevant information about its movement and the environment, and a variety of tools can be found in the literature to obtain that information and to process it. Encoders permit calculating the odometry of the robot and they are one of the main sensors used in mobile robotics, but the information obtained from the encoders should not be used as an absolute measure because of the error they accumulate. Taking that into account, in real implementations, odometry data are complemented by other sensors. For instance, it can be complemented with sonars [13] or lasers [14]. In addition, mobile robots can work also with GPS, which presents a good performance in outdoor applications but it has little ability to offer signal in indoor or narrow outdoor zones. Complementing these sensors, visual sensors constitute currently the principal area to investigate new approaches to robot navigation. Cameras offer a lot of information from the environment with a relatively low cost, they can be used in outdoor and indoor environments and they permit solving other high-level tasks such as human identification, face recognition [15] and obstacles detection [16]. There are some examples of sensors combinations: For example [PERSON] et al. [17] present a system that combines sonar with visual information to perform SLAM (Simultaneous Localization And Mapping) tasks. More information about these systems can be found in [18], where a review of these techniques is done. The visual information can be captured with a variety of devices; from a single camera [19], stereo cameras [20] or omnidirectional cameras [21; 22]. Solving navigation tasks using only visual information is a challenging problem. Images contain much information and relevant data should be extracted from them in order to ease the mapping and localization tasks. The use of local descriptors was the classical approach for obtaining relevant information from the images and the method consists of extracting outstanding landmarks or regions and describing them using algorithms such as SIFT (Scale-Invariant Feature Transform) [23], SURF (Speeded-Up Robust Features) [24] or BRIEF (Binary Robust Independent Elementary Features) [25]. This is a mature alternative and many researchers make use of it in mapping and localization. For example, [PERSON] et al. [26] proposed using these descriptors to perform topological localization. [PERSON] and [PERSON] [7] did that work in outdoor areas. [PERSON] et al. [27] present a comparative study for the localization task using local-appearance descriptors in indoor environments. A comparative evaluation of this kind of local appearance methods was made by [PERSON] et al. [28]. They evaluated the repeatability, the invariance against small changes and distinctiveness of the descriptors under different perceptual conditions. They proved that the behaviour of local appearance descriptors can deteriorate when unexpected conditions appear. Taking it into account that the information not only changes while the robot is moving and the perspective varies, but also due to changing lighting conditions, occlusions by furniture or people and noise during the acquisition, it is necessary to calculate more robust and invariant descriptors. In this sense, global-appearance descriptors constitute a powerful alternative, which consists of describing each image as a whole, without detecting any landmark or local feature. This method creates a more intuitive representation of the environment, simplifies the navigation process and permits building topological maps of the environment. Since each image is described with a unique vector, localization can be carried out with simple algorithms, based on the pairwise comparison of vectors.