This brief presents an adaptive output feedback image-based visual servoing (IBVS) law for a quadrotor unmanned aerial vehicle. The control objective is to regulate the relative 3-D position and yaw of the vehicle to a planar horizontal visual target consisting of multiple points. The control is implemented using a minimal number of commonly available low-cost on-board sensors including a strapdown inertial measurement unit and a monocular camera. The IBVS method relies on moment image features which are defined using a virtual camera. Output feedback introduces a filter to the control which removes the common requirement for linear velocity measurement. The method is adaptive and compensates for a constant force disturbance appearing the translational dynamics and parameter uncertainty in thrust constant, desired feature depth, and mass. Exponential stability of the outer loop and combined inner-outer closed-loop error dynamics is proven. Flight tests demonstrate the proposed method's motion control performance and its ability to compensate parametric uncertainty and reject constant force disturbances.

This letter for the first time extends the virtual camera image-based visual servoing (IBVS) scheme to enable an underactuated rotorcraft UAV to regulate its translational motion and heading relative to a planar visual target of arbitrary orientation. The conversion from real camera images to virtual camera images of visual targets are proposed based on a set of rotation matrices. Hence, image moment features can be reused due to the simplicity and decoupled structure of the interaction matrix, and satisfactory 3D Cartesian trajectory of UAVs. In the design of the IBVS control law, the external disturbance and model uncertainties are estimated by an integral-based filter. In addition, to enable tracking of a moving visual target, a velocity estimator is developed. The global asymptotic stability of the error dynamics is proven. Both of the simulation and experimental results of tracking of a tilted moving planar target are provided to show the efficacy of the proposed IBVS scheme.

This paper presents an adaptive output feedback-based visual servoing law for a quadrotor unmanned aerial vehicle equipped with a single downward facing camera. The objective is to regulate the relative position and yaw of the vehicle to a planar target consisting of multiple points using a minimal sensor set, i.e., an inertial measurement unit and a vision sensor. A set of first order image moment features, defined in the image plane of a virtual camera with zero roll and pitch motion, is used for visual servoing. It has been observed in previous work that various system uncertainties, such as aerodynamics constants and attitude estimation bias, result in steady-state errors if not being compensated. By treating those uncertainties as unknown system parameters, an adaptive backstepping controller is developed. As the given visual servoing law is an output feedback controller, the translational velocity measurement from the global position system is not required. The asymptotic stability of the error dynamics is proven. Experimental results are provided to demonstrate the controller performance.

This paper presents a dynamic image-based visual servoing (IBVS) control law for a quadrotor Unmanned Aerial Vehicle (UAV) equipped with a single fixed on-board camera. The motion control problem is to regulate the relative position and yaw of the vehicle to a stationary target located on the ground. The control law is termed dynamic as it based on the dynamics and kinematics of the vehicle. The proposed design relies on the notion of a virtual camera and image moments as visual features to simplify the kinematics and dynamics. The convergence of the closed-loop is proven to be globally asymptotically stable. Experimental and simulation results demonstrate the method's ease of on-board implementation, performance, and robustness.