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Sensors 2017, 17(5), 1061; doi:10.3390/s17051061

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

Intelligent Systems Lab, Universidad Carlos III de Madrid, Leganes, 28911 Madrid, Spain
Current address: Department of System Engineering and Automation, Avenida de la Universidad, 30, Leganes, 28911 Madrid, Spain.
These authors contributed equally to this work.
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Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 19 December 2016 / Revised: 19 April 2017 / Accepted: 4 May 2017 / Published: 7 May 2017
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Abstract

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. View Full-Text
Keywords: obstacle detection; collision avoidance; size expansion; feature points; UAV; monocular vision obstacle detection; collision avoidance; size expansion; feature points; UAV; monocular vision
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Al-Kaff, A.; García, F.; Martín, D.; De La Escalera, A.; Armingol, J.M. Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs. Sensors 2017, 17, 1061.

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