# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 1 Introduction  
Over the last few years, mobile robotics has become a technology that has gained presence in many kinds of environments to solve different problems, both in industries, educative centres and in households. To increase their range of applications, mobile robots must be able to solve the task they have been designed for in a truly autonomous way. With this aim, two crucial abilities must be developed: the robot must be able to build a model of the environment where it has to move and to estimate its position and orientation within this model. Mobile robots may be equipped with different kinds of sensors that provide them with information that permits solving the mapping and localization problems. These sensors can be categorised into proprioceptive and exteroceptive. On the one hand, proprioceptive sensors measure the state of the robot. Encoders installed in the wheels are an example. Through an odometry process they permit estimating the displacement of the robot, but the error in this estimation tends to grow indefinitely. This is why encoders tend to be used in combination with other sources of information [1]. On the other hand, exteroceptive sensors measure some information from the environment where the robot is moving. Among them, some researchers have made use of GPS, SONAR and laser sensors in previousworks. First, GPS (Global Positioning Systems) constitute a robust choice outdoors, but the information they provide close to buildings and on narrow streets is less reliable [2]. Second, SONAR and laser sensors permit measuring the distance to the objects around the robot using different technologies. The use of laser sensors has extended substantially in mobile robotics [3] because they tend to present an improved precision and angular resolution comparing to SONAR. Nevertheless, they also present a higher cost, weight and power consumption. More recently, vision sensors have gained popularity because they present several advantages. They capture a big quantity of information from the environment, which can be used to carry out many high level tasks, apart from mapping and localization, such as people and objects detection and recognition. They also present a relatively low cost and power consumption comparing to laser rangefinders and the information they provide is stable both outdoors and indoors, unlike GPS, whose signal tends to degrade indoors. Vision sensors can be used either as the only source of information from the environment or in combination with other kinds of sensors [4]. Initially, monocular configurations were used but later works tried to expand the field of view through other configurations such as binocular [5] or omnidirectional [6; 7; 8]. In recent years, the use of omnidirectional vision sensors has expanded thanks to the big quantity of information they capture (as they are able to take images with a field of view of 360 deg around the robot) with a relatively low cost. Nevertheless, working with omnidirectional visual data is a complex task when the robot has to create models of large and complex environments while moving with more than 3 Degrees Of Freedom (DOF). In these cases, it is necessary to extract some information from the scenes to build a robust model that gathers relevant and invariant knowledge from the environment and that permits estimating the position of the robot with the required DOF. Extracting this useful information from the scenes is a key point since images are very high dimensional data that change not only when the robot moves along any DOF but also under other circumstances such as variations in lighting conditions. Two main approaches have been used by the researchers to compile such information. On the one hand, some relevant landmarks or outstanding points or regions (either natural or artificial) can be extracted and described using any local descriptor that captures the appearance of the landmarks' neighbourhood trying to get invariance to position, scale and rotation [9; 10; 11; 12].