Simultaneous Localization And Mapping (SLAM) is a well-known problem in a variety of cases, such as ground robots, drones, cars, and underwater robots. To perform efficiently SLAM is even more crucial when autonomous navigation must be guaranteed. UAVs are widely taking the scene in many applications, like search and rescue missions, real-estate applications, agriculture, system logistic, and package delivery, thanks to their dexterity in movements, even in narrow environments. Many of the state-of-the-art SLAM algorithms find a common factor in the usage of Visual - Visual Inertial Odometry (VO-VIO). Indeed, more and more solutions to the problem are developed, bringing new approaches, or improving existing ones.