**1. Introduction**

One of the first uses of micro air vehicles (MAVs) was during the World War I [1]. Since then, MAVs have been considered as a promising technology, and nowadays they are being used in several different tasks, such as agriculture [2], patrolling [3], mapping [4], and delivering [5]. Compared to conventional human-crewed aerial vehicles, MAVs are a low-cost and entirely suitable alternative for repetitive or high-precision demanding tasks. Besides, they are also recommended for low-altitude flights and for those that demand a high range of maneuvers.

The estimation of the MAVs' position, i.e., its localization in the world, is the main common requirement between all the before mentioned tasks, even if they would be addressed by other types of mobile robots rather than MAVs. For such complex tasks, localization and navigation are fundamental capabilities that allow MAVs to accomplish their mission [6]. The localization

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for MAVs is usually solved by the global positioning system (GPS) [7], in which an embedded GPS sensor communicates with different satellites that are orbiting the Earth to estimate the position. Other MAV models rely on different ways of estimating their position, such as inertial sensors or visual odometry algorithms. However, most of these models do not have a second position estimation system in addition to the primary system, i.e., the GPS [7]. Therefore, the MAVs that depend exclusively on GPS to estimate their position are more likely to fail on their tasks or missions; once like any other sensor, the GPS might fail, and they do not have a redundant position estimation system.

Even though it is widely used in different situations and for distinct goals, the GPS sensor is vulnerable to some problems [8, 9]. The amount of satellites that are available to establish a communication with the GPS influences the position estimation certainty, as well as the signal quality between them. The signal might be affected by the weather, such as cloudy and rainy days, and by obstacles, like high buildings or hills. Hence, the higher is the number of connected satellites and the stronger is the signal, the lower is the GPS position estimation error. In addition to this GPS weakness, there is another problem that might disturb the GPS position estimation, the so-called Jamer guns. While the GPS sensor is reading the satellite signal to estimate the position, these guns jam the signal, and hence, the estimation becomes unreliable [10, 11].

The mobile robotics research community has investigated the MAV position estimation problem, and valuable works have been proposed. In general, it is addressed by them as the localization problem from the mobile robotics field, in which the goal is to estimate the pose of a robot, based on readings of its sensors, in an a priori known map [12]. The works proposed by the community covers a considerable variety of approaches, in which the main differences are the kind of data used to represent the environment and the technique used to estimate the pose. Despite this diversity, one characteristic that most of them share is the use of visual data from cameras to estimate the localization. This choice is made due to the advantages of cameras to deal with this problem in comparison to the other sensors, such as the low weight for MAVs, the distinct information from one image (color, depth, intensity, etc.), and the longdistance range for the readings.

This chapter covers the most important proposed works that aimed to deal with visual MAV localization problem. As aforementioned, there are two main topics that are worthy to be covered when presenting this kind of works, which are the data used as a map and how the estimation is calculated. Therefore, this chapter first presents a discussion about different maps used so far, followed by the review of the localization itself. In addition to detailing and comparing them, it also presents what the next trends or future work for this problem are.
