Abstract
To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV–Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance.
Highlights
Nowadays, unmanned aerial vehicles (UAVs), computer vision techniques, and flight control systems have received great attention from the research community in robotics
The proposed cooperative Unmanned Aerial Vehicles (UAVs)–Target visual-SLAM method is validated through computer simulations
The simulated UAV–Target environment is composed of 3D landmarks, which are randomly distributed over the ground
Summary
Nowadays, unmanned aerial vehicles (UAVs), computer vision techniques, and flight control systems have received great attention from the research community in robotics. This interest has resulted in the development of systems with a high degree of autonomy. UAVs are very versatile platforms and very useful for several tasks and applications [1,2]. In this context, a fundamental problem to solve is the estimation of the positions of UAVs. For most applications, GPS In scenarios where GPS signals are jammed intentionally [3] or when the precision error can be Sensors 2020, 20, 3531; doi:10.3390/s20123531 www.mdpi.com/journal/sensors
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