In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target reprojection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.
In this paper, we investigate a range of image-based visual servo control algorithms for regulation of the position of a quadrotor aerial vehicle. The most promising control algorithms have been successfully implemented on an autonomous aerial vehicle and demonstrate excellent performance. In this paper we propose a new control method for quadrotor autonomous landing on a visual target without linear velocity measurements. Only onboard sensing is exploited, such that only the images of the landing pad from a down-looking camera, along with data from an Inertial Measurement Unit's gyro, are used. The control system consists of an image-based nonlinear observer that estimates online the linear velocity of the vehicle and a backstepping image-based controller that generates attitude, and thrust setpoints to the quadrotor autopilot. Both observer and controller share the same feedback information: spherical visual features. Therefore no further image elaboration is needed for the estimation. This, along with the fact that only simple computations on low- and constant-dimension arrays are involved, makes the proposed solution computationally cheap. Real-hardware experiments on a quadrotor are carried out to verify the validity of the proposed control system.