Distance and velocity estimation using optical flow from a monocular camera

Abstract

Monocular vision is increasingly used in Micro Air Vehicles for navigation. In particular, optical flow, inspired by flying insects, is used to perceive vehicles’ 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 to use an extended Kalman filter to estimate the distance and velocity of the vehicles from optical flow while approaching a surface, and then use these estimates for control. We implement and test our algorithm in computer simulation and on-board a Parrot AR.Drone 2.0 to demonstrate its feasibility for MAVs landings. Both results show that the algorithm is able to estimate height and velocity of the MAV accurately.