A survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems

McFadyen, Aaron; Mejias, Luís

doi:10.1016/j.paerosci.2015.10.002

Cited by 73 publications

(33 citation statements)

References 125 publications

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“…The main challenge in achieving autonomous detect and avoid is reliably detecting aircraft with sensors that can be carried on UAS due to cost, size, weight, and power considerations. For small‐to‐medium sized fixed‐wing UAS, vision is the leading sensing technology for detecting aircraft that are uncooperative (in the sense that they do not share information) . While vision‐based techniques for reliably detecting aircraft against sky backgrounds have been experimentally demonstrated, only partial progress has been made in the detection of aircraft against structured backgrounds such as the ground clutter in Figure .…”

Section: Introductionmentioning

confidence: 99%

“…Over the past decade, several research and industry groups have conducted flight experiments that demonstrate the feasibility of detecting potential collision threats using vision (see Pellebergs and Mcfadyen and Mejias and references therein). Most recently, real‐time vision‐based aircraft detection has been demonstrated on unmanned aircraft, and large‐scale experiments have been conducted using manned aircraft as surrogate UAS platforms for data capture .…”

Section: Introductionmentioning

confidence: 99%

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Detection of aircraft below the horizon for vision‐based detect and avoid in unmanned aircraft systems

Molloy

Ford

Mejias

2017

Journal of Field Robotics

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Vision‐based aircraft detection technology may provide a credible sensing option for automated detect and avoid in small‐to‐medium size fixed‐wing unmanned aircraft systems (UAS). Reliable vision‐based aircraft detection has previously been demonstrated in sky‐region sensing environments. This paper describes a novel vision‐based system for detecting aircraft below the horizon in the presence of ground clutter. We examine the performance of our system on a data set of 63 near collision encounters we collected between a camera‐equipped manned aircraft and a below‐horizon target. In these 63 encounters, our system successfully detects all aircraft, at an average detection range of 1890 m (with a standard error of 43 m and no false alarms in 1.1 h). Furthermore, our system does not require access to inertial sensor data (which significantly reduces system cost) and operates at over 12 frames per second.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of aircraft below the horizon for vision‐based detect and avoid in unmanned aircraft systems

Molloy

Ford

Mejias

2017

Journal of Field Robotics

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“…Recently, Mcfadyen et al presented a literature review of the vision-based collision avoidance systems [35]. …”

Section: Related Workmentioning

confidence: 99%

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

Al-Kaff

García

Martín

et al. 2017

Sensors

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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.

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“…Additionally, the safety of the technology and its potential for accidents are viewed with increasing scepticism. However, important efforts are being dedicated to develop sense and avoid technologies (Gökçe et al 2015;Yu and Zhang 2015;Mcfadyen and Mejias 2016), and there is much interest to establish clear legislation. Other limitations of the UAV technology for monitoring infrastructure are technical aspects like the need for specialist expertise, the lack of standards and an insufficiently developed range of image analysis techniques (Kelcey and Lucieer 2012;Laliberte et al 2011).…”

Section: Advantages and Limitations Of Small Uavs For Monitoring Pipementioning

confidence: 99%

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Gómez

Green

2017

Arab J Geosci

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Oil and gas transmission pipelines require monitoring for maintenance and safety, to prevent equipment failure and accidents. Unmanned aerial vehicles (UAVs) technology is emerging as an opportunity to supplement current monitoring systems. Small UAV technological solutions are flexible and adaptable and with a demonstrated capacity to obtain valuable data at small to medium spatial scales. Systematic surveys of extensive areas are better completed with fixed-wing platforms and automatic flight design, whilst multirotor platforms provide flexibility in shorter and localized inspection missions. The type of sensor carried by an aerial platform determines the sort of data acquired and the obtainable information; sensors also determine the need for specific mechanical designs and the provision of energy on-board required from the system. UAV systems prototyped to monitor pipelines are reviewed in this paper, and a number of monitoring scenarios are proposed and illustrated. Notwithstanding difficulties encountered in the generalization of use for civilian applications, small UAVs have demonstrated, through research and operational cases, the capacity to support the inspection and monitoring of oil and gas pipelines.

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A survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems

Cited by 73 publications

References 125 publications

Detection of aircraft below the horizon for vision‐based detect and avoid in unmanned aircraft systems

Detection of aircraft below the horizon for vision‐based detect and avoid in unmanned aircraft systems

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

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

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