Biologically inspired UAV obstacle avoidance and control using monocular optical flow & divergence templates

Abstract

This paper describes a biologically inspired flight control strategy for unpiloted aerial vehicles (UAVs) using optical flow and depth information. The scene depth map is generated by measurement of optical flow in a uniform grid across the image. Grid regions are each analyzed singularly, each ones depth is estimated by comparing their observed motion with the motion of the craft as measured by the inertial measurement unit. Two control processes run simultaneously on the robot, a gross strategy uses divergence of optical flow vectors about the focus of expansion, balancing these according to a predefined divergence template. A fine strategy looks for outliers from the estimated depth map. The sum of these processes generates a control impulse to steer a quadrotor helicopter away from obstacles. The algorithm was tested against ground-truth datasets and real flight. In both situations it was able to control the craft heading and pitch during indoor operation, avoiding both corridor walls and oncoming obstacles.