Vision-based detection and tracking of aerial targets for UAV collision avoidance

Mejias, Luís; McNamara, Scott; Lai, John; Ford, Jason

doi:10.1109/iros.2010.5651028

Cited by 68 publications

(37 citation statements)

References 15 publications

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“…Mejias et al [11][12][13] and Dey et al [14] describe different vision-based collision avoidance systems for which real flights were performed in order to collect suitable test data. To the author's knowledge, none of those databases have been made available for further development by the research community.…”

Section: Related Workmentioning

confidence: 99%

SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

Carrio

Collumeau

et al. 2015

J Intell Robot Syst

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Unmanned Aerial Systems (UASs) have recently become a versatile platform for many civilian applications including inspection, surveillance and mapping. Sense-and-Avoid systems are essential for the autonomous safe operation of these systems in non-segregated airspaces. Vision-based Sense-andAvoid systems are preferred to other alternatives as their price, physical dimensions and weight are more suitable for small and medium-sized UASs, but obtaining real flight imagery of potential collision scenarios is hard and dangerous, which complicates the development of Vision-based detection and tracking algorithms. For this purpose, user-friendly software for synthetic imagery generation has been developed, allowing to blend user-defined flight imagery of a simulated aircraft with real flight scenario images to produce realistic images with ground truth annotations. These are extremely useful for the development and benchmarking of Vision-based detection and tracking algorithms at a much lower cost and risk. An image processing algorithm has also been developed for automatic detection of the occlusions caused by certain parts of the UAV which carries the camera. The detected occlusions can later be used by our software to simulate the occlusions due to the UAV that would appear in a real flight with the same camera setup. Additionally this algorithm could be used to mask out pixels which do not contain relevant information of the scene for the visual detection, making the image search process more efficient. Finally an application example of the imagery obtained with our software for the benchmarking of a state-of-art visual tracker is presented.

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Section: Related Workmentioning

confidence: 99%

SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

Carrio

Collumeau

et al. 2015

J Intell Robot Syst

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“…Optical sensors have been shown effective for obstacle avoidance and altitude control in drones [18]. Few works investigate use of cameras on UAVs for measuring the relative direction of large moving aircrafts relative to the background scene [19], [20]. Limited field of view, high dependency on visual contrast and light conditions, failure when the target is stationary or located on non-uniform or cluttered backgrounds, high computational demand for processing high resolution and high frame rate images [20] are some of the drawbacks of this approach.…”

Section: Introductionmentioning

confidence: 99%

“…Few works investigate use of cameras on UAVs for measuring the relative direction of large moving aircrafts relative to the background scene [19], [20]. Limited field of view, high dependency on visual contrast and light conditions, failure when the target is stationary or located on non-uniform or cluttered backgrounds, high computational demand for processing high resolution and high frame rate images [20] are some of the drawbacks of this approach. To the best of our knowledge, the only onboard relative positioning systems demonstrated on real drones are based on infra-red (IR) sensors [21], [22] for indoor flying robots.…”

Section: Introductionmentioning

confidence: 99%

On-Board Relative Bearing Estimation for Teams of Drones Using Sound

Basiri

Schill

Lima³

et al. 2016

IEEE Robot. Autom. Lett.

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Abstract-In a team of autonomous drones, individual knowledge about the relative location of teammates is essential. Existing relative positioning solutions for teams of small drones mostly rely on external systems such as motion tracking cameras or GPS satellites that might not always be accessible. In this letter, we describe an onboard solution to measure the 3-D relative direction between drones using sound as the main source of information. First, we describe a method to measure the directions of other robots from perceiving their engine sounds in the absence of self-engine noise. We then extend the method to use active acoustic signaling to obtain the relative directions in the presence of self-engine noise, to increase the detection range, and to discriminate the identity of robots. Methods are evaluated in real world experiments and a fully autonomous leader-following behavior is illustrated with two drones using the proposed system.

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“…And some researches, e.g. [14], have presented how to detect and track the point-like UAS with the far distance only using the visual information.…”

Section: Introductionmentioning

confidence: 99%

UAS see-and-avoid strategy using a fuzzy logic controller optimized by Cross-Entropy in Scaling Factors and Membership Functions

Olivares-Méndez

Campoy

et al. 2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall.In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

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Vision-based detection and tracking of aerial targets for UAV collision avoidance

Cited by 68 publications

References 15 publications

SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

On-Board Relative Bearing Estimation for Teams of Drones Using Sound

UAS see-and-avoid strategy using a fuzzy logic controller optimized by Cross-Entropy in Scaling Factors and Membership Functions

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