Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.
@cite_19: An original configuration of a small aerial vehicle having eight rotors is presented. Four rotors are devoted to the stabilization of the orientation of the helicopter, and the other four are used to drive the lateral displacements. A precompensation on the roll and pitch angles has been introduced so that the attitude dynamics is practically independent of the translational dynamics. This compensation is directly related to the velocity of the lateral motors. The dynamical model is obtained using the Euler-Lagrange approach. The proposed configuration is particularly useful for image processing since the the camera orientation is held constant. The eight-rotor rotorcraft is simpler to pilot than other rotorcrafts. A control strategy is proposed that uses the optical flow measurements to achieve a hover flight that is robust with respect to perturbations like wind. The new aerial configuration and control strategy have been tested in real-time experiments.
@cite_13: The problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow.
@cite_23: For the control of unmanned aerial vehicles UAVs in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit IMU. First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the unknown metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter EKF formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions.
@cite_2: 
In related research, several works have achieved optical flow based control of a MAV , e.g., @cite_23 @cite_19 @cite_13 . As mentioned in the introduction, the standard optical flow methods are computationally too heavy to run on a quadcopter of less than 50 g. For instance, have flown with a 25 g quadcopter before, while computing optical flow for visual odometry @cite_2 . However, this was done on an external computer. As miniaturization of hardware also poses a limitation on communication bandwidth, this can result in a significant delay in the controls. To obtain full autonomy, it would be wise to uncouple a MAV of any external dependencies.