## 3 Review of Global Appearance Descriptors  
In this section, some alternatives to extract global information from panoramic images are summarized. They are known as global-appearance descriptors and they try to keep relevant data with low memory requirements. The visual sensor used in the experiments takes omnidirectional images, for that reason the first step is to transform them into panoramic ones. The starting point is a panoramic image _i_(_x_,_y_) \(\in\mathbb{R}^{N_{x}\times N_{y}}\) and after using any of the global appearance methods the result is a descriptor \(\vec{d}\in\mathbb{R}^{t\times 1}\) where \(t\) is the size of the descriptor, as detailed in the next subsections. Firstly it is necessary to divide the panoramic image in a set of cells. Depending on the shape and number of cells, a different descriptor is obtained. In this work descriptors were built in two different ways. The classic method, described in [48], divides the image in uniformly distributed and non-overlapped horizontal cells. The main idea is that by using panoramic images the descriptor will be invariant to pure rotations of the robot in the ground plane. That is possible due to the fact that the information in the each row is the same and the only change is a horizontal shift in each row. This option was tested in some navigation tasks such as pure visual localization [38], hierarchical localization [3] or topological maps compression [9]. The second method used in this paper consists of defining a set of vertical cells with some overlapping between consecutive cells. To obtain a matching method invariant to the robot orientation, two steps are needed. The descriptor is built by putting together information from the vertical cells, so, the algorithm that compares two descriptors needs, first, to calculate the difference of orientation between both descriptors and shift one of them, by removing its first columns and appending them at the end of the vector. To achieve enough resolution in this step, the descriptor is built with overlapping between consecutive cells. Once the relative orientation is the same, both descriptors can be compared in a straightforward way. Using this additional step that normalizes the orientation, this method also becomes invariant to robot orientation. A comparison between these two methods to build global-appearance descriptors is introduced in [49]. Figure 1 shows how the cells are defined in each of the two methods. Throughout the paper, the descriptors calculated with horizontal cells are named position descriptors, whereas the descriptors calculated with vertical cells are named orientation descriptors. The objective of using both approaches to build descriptors is twofold. On the one hand, information obtained by descriptors based on pure robot position and by descriptors that are influenced on its orientation are taken into account. On the other hand, this idea could help reduce perceptual aliasing, (i.e., different locations may generate similar visual descriptors). Combining information obtained by horizontal and vertical cells can provide more reliable results as far as image matching is concerned. These techniques are invariant against changes in the orientation of the robot if it moves in the floor plane and panoramic images are used. To capture them the mobile robot is equipped with an omnidirectional vision system. This system consists of a camera and a hyperbolic mirror mounted in front of the lens. The system is mounted vertically on the mobile robot, as done in several previous works such as [50; 51; 52; 53]. The omnidirectional camera captures images with a field of view of 360\({}^{\circ}\) around the robot so they offer complete information from the surroundings of the robot from every capture point. Finally, the omnidirectional images might be transformed into panoramic images. The complete experimental setup is presented in Section 5. Once the methods to divide the image were explained, different approaches to efficiently and robustly describe each region are presented.