Micro Air Vehicles need to have a robust landing capability, especially when they operate outside line-of-sight. Autonomous landing requires the identification of a relatively flat landing surface that does not have too large an inclination. In this article, a vision algorithm is introduced that fits a second-order approximation to the optic flow field underlying the optic flow vectors in images from a bottom camera. The flow field provides information on the ventral flow (V x /h), the time-to-contact (h/ -V z ), the flatness of the landing surface, and the surface slope. The algorithm is computationally efficient and since it regards the flow field as a whole, it is suitable for use during relatively fast maneuvers. The algorithm is subsequently tested on artificial image sequences, hand-held videos, and on the images made by a Parrot AR drone. In a preliminary robotic experiment, the AR drone uses the vision algorithm to determine when to land in a scenario where it flies off a stairs onto the flat floor.NOMENCLATURE V x , V y , V z velocities in the body frame (m/s) X, Y, Z world coordinates aligned with the body frame (m) h height (m) ω x = V x /h ventral flow in X -direction (s -1 ) ω y = V y /h ventral flow in Y -direction (s -1 ) ω z =

Bio-inspired methods can provide efficient solutions to perform autonomous landing for Micro Air Vehicles (MAVs). Flying insects such as honeybees perform vertical landings by keeping flow divergence constant. This leads to an exponential decay of both height and vertical velocity, and allows for smooth and safe landings. However, the presence of noise and delay in obtaining flow divergence estimates will cause instability of the landing when the control gains are not adapted to the height. In this paper, we propose a strategy that deals with this fundamental problem of optical flow control. The key to the strategy lies in the use of a recent theory that allows the MAV to see distance by means of its control instability. At the start of a landing, the MAV detects the height by means of an oscillating movement and sets the control gains accordingly. Then, during descent, the gains are reduced exponentially, with mechanisms in place to reduce or increase the gains if the actual trajectory deviates too much from an ideal constant divergence landing. Real-world experiments demonstrate stable landings of the MAV in both indoor and windy outdoor environments. Index Terms-Biologically-inspired robots, aerial robotics, visual servoing, optical flow, autonomous landing.H. W. Ho is with the Micro Air Vehicle laboratory

Monocular vision is increasingly used in micro air vehicles for navigation. In particular, optical flow, inspired by flying insects, is used to perceive vehicle movement with respect to the surroundings or sense changes in the environment. However, optical flow does not directly provide us the distance to an object or velocity, but the ratio of them. Thus, using optical flow in control involves nonlinearity problems which add complexity to the controller. To deal with that, we propose an algorithm that estimates distance and velocity of the vehicle based on optical flow measured from a monocular camera and the knowledge of control inputs. This algorithm applies an extended Kalman filter to state estimation and uses the estimates for landing control. We implement and test our algorithm in computer simulation and on board a Parrot AR.Drone 2.0 to demonstrate its feasibility for micro air vehicles landings. Results of the simulation and multiple flight tests show that the algorithm is able to estimate height and velocity of the micro air vehicles accurately, and achieves smooth landings with these estimates, even in windy outdoor environments.