A study of vision-based autonomous flight method for UAV

Abstract

In recent years, autonomous delivery system for UAV have been developed actively to improve logistical efficiency. Delivering to a small living space requires autonomous flight abilities such as obstacle avoidance and pinpoint landing. This study aims to develop a vision-based autonomous flight system for UAV that performs self localization and obstacle avoidance simultaneously based only on monocular camera images. Self localization is realized by estimating the position and the inclination of the AR-marker. Obstacle avoidance is realized by predicting the depth of a single image using Convolutional Neural Network(CNN). Verification experiments confirmed that proposed system has ability to fly and land UAV toward the AR-marker while avoiding obstacles even in a non-GPS environment.