# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 1 Introduction
On the other hand, each scene can be represented through a unique global appearance descriptor that contains information on the whole scene [13; 14; 15; 16]. Many authors have addressed the mapping and localization problems using vision sensors. The different approaches proposed can be roughly classified into three different types, depending on the contents and the internal structure of the models. First, a metric map can be built trying to define the position of some outstanding points of the environment with respect to a reference system. These models permit estimating the position of the robot with geometrical accuracy up to a specific error [17; 18; 19]. Second, topological maps usually represent some characteristic places of the environment and the connectivity relationships between them. They tend to be simpler representations but they contain usually enough details for most applications [20]. Third, hybrid maps try to gather the advantages of the two previous approaches. They arrange the information into several layers with different levels of detail, containing topological models in the top layers that permit a rough localization and metric models in the bottom layers to refine this localization [21; 22; 23]. Traditionally, metric maps have been built using methods based on the extraction, description and tracking of local features along a set of scenes captured by the vision sensor mounted on the robot. This information is often combined with other sources of information, such as odometry or laser [24]. However, these models tend to be quite complex and not easily interpretable by a human operator, and the localization process is usually elaborated and computationally expensive. In contrast, topological maps offer more intuitive representations of the environment [25]. Visual global-appearance approaches may be used to create such models, since no metric information can be extracted from global descriptors. They lead to simpler models where the localization process is more straightforward, based mainly on the pairwise comparison between image descriptors [26]. The problem of robot localization using visual models of the environment has been thoroughly studied when the trajectory of the robot is contained in the ground plane. Researchers have made use both of local features [27] and global appearance [28] to build image descriptors that permit solving this problem. Beyond that, in some applications, it may be useful to estimate the altitude of the robot with respect to this plane, without changing the visual map nor including any further visual information. The proposal of the present work fits within this area. The main objective consists in extracting some information from the images that permits estimating the relative altitude of the robot, using the previously built visual model of the environment. About the choice of the approach to describe the scenes, some researchers have addressed the altitude estimation problem using local descriptors [29; 30; 31; 32]. However, the literature on altitude estimation using only visual information and global appearance descriptors is quite sparse and few works can be found on this topic, despite the advantages that global appearance descriptors can offer to the mapping and localization problems [33]. Taking this into account, this work is focused on studying the performance of global-appearance methods to solve this problem. The only source of information used is a catadioptric vision sensor mounted on the mobile platform. The contribution of this paper is twofold. On the one hand, some global appearance approaches are defined to incorporate the altitude information in the descriptors and some methods are proposed to estimate robustly this relative altitude without changing nor including any additional information to the visual model of the environment. On the other hand, a comparative evaluation of these methods is carried out to analyse their behaviour both indoors and outdoors.