Open AccessObstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision
Author(s) -
N Aswini,
S. Uma
Publication year - 2019
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v9i5.pp3504-3511
Subject(s) - drone , obstacle avoidance , computer science , computer vision , artificial intelligence , obstacle , monocular vision , scale invariant feature transform , software , automation , feature (linguistics) , monocular , feature extraction , robot , mobile robot , engineering , political science , law , mechanical engineering , programming language , linguistics , genetics , philosophy , biology
Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro.