MAV indoor navigation based on a closed-form solution for absolute scale velocity estimation using Optical Flow and inertial data

Abstract

A new vision-based obstacle avoidance technique for indoor navigation of Micro Aerial Vehicles (MAVs) is presented in this paper. The vehicle trajectory is modified according to the obstacles detected through the Depth Map of the surrounding environment, which is computed online using the Optical Flow provided by a single onboard omnidirectional camera. An existing closed-form solution for the absolute-scale velocity estimation based on visual correspondences and inertial measurements is generalized and here employed for the Depth Map estimation. Moreover, a dynamic region-of-interest for image features extraction and a self-limitation control for the navigation velocity are proposed to improve safety in view of the estimated vehicle velocity. The proposed solutions are validated by means of simulations.