**2.2. The future of UAV localization systems**

Despite the great effort of the research community to deal with the MAV localization problem, there is no solution that works for all the possible environments and constraints. This problem varies considerably, such as the environment, which can be either indoor or outdoor, the knowledge about the initial position, whether it is known or not, and the amount of MAVs that are being localized, which can be single or multiple MAVs. Despite these difficulties, there is also the map issue, which is caused either by a low updating frequency, such as a satellite that takes some time to revisit a specific area, or by a quick environmental change, like a snowy day that makes the whole environment become white.

To deal with the problem of the type of environment, the approaches that seem more likely to work are the one that recognizes the objects within the environment and the other that deals


**Table 1.** Comparison between all the discussed works in terms of different information.

with 3D structures. Hence, independently of being indoor or outdoor, it would be possible to recognize objects or detect the environment's shape in both scenarios, to then continue the pose estimation calculation. On the other hand, the problem of the outdated map could be overcome by using deep learning, which provides robust solutions for different seasons or illumination changes in images [33]. Another solution that is possible through the use of deep learning is to teach a net to differentiate roads, buildings, and forest, to then segment both the map and the MAV sensor readings. Hence, instead of, for instance, matching the color of different pixels from MAV images and patches from the 2D satellite image map, the matching would be done considering the classes of the environment, avoiding the problems caused by color or illumination changes.

Due to the fact that MAV is a certainly popular technology and that it is being used in many different tasks, another promising matter that should be investigated is the localization system for multiple MAVs. As there will be even more MAVs flying and cooperating in the future, it is essential to have localization approaches that take advantage of the high amount of MAVs available in the air and, therefore, improve the pose estimation.
