Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

Al-Kaff, Abdulla; Meng, Qinggang; Martín, David; Escalera, Arturo de la; Armingol, José María

doi:10.1109/ivs.2016.7535370

Cited by 45 publications

(22 citation statements)

References 23 publications

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“…As a recent technique, Al-Kaff et al's technique (Al-Kaff, 2016) is able to some extent to detect obstacles in complex environments. However, it cannot separate far obstacles from close ones.…”

Section: Resultsmentioning

confidence: 99%

“…This method is applicable only when the background is simple, but it is unfunctional when the background is complex. Al-Kaff et al (Al-Kaff, 2016) in another study introduced a technique for searching and detecting obstacles, which mimics human's concept of obstacle existence. In Al-Kaff et al (AlKaff, 2016) method, obstacle is the object that is enlarging in consecutive captured frames.…”

Section: Research Backgroundmentioning

confidence: 99%

“…One of the main features of obstacle detection algorithms is their application in complex environments; thus, Al-Kaff et al's method (Al-Kaff, 2016) is to some extent capable of illustrating obstacle zone in complex environments, while Zeng et al's method (Zeng, 2016) and other brain-inspired techniques are not efficient in such cases. That's why Al-Kaff et al's algorithm (Al-Kaff, 2016) has superiority and preference thanks to its application in complex environments, but it has failings as well; since it does not distinguish between close and far obstacles.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Brain-inspired methods use a similar technique based on how human understands and detects obstacles. Various studies have been performed about braininspired and mono-based techniques (Mori, 2013), (Al-Kaff, 2016), (Zeng, 2016). One of the key features of obstacle detection algorithms is their functionality in complex environments.…”

Section: Introductionmentioning

confidence: 99%

“…One of the key features of obstacle detection algorithms is their functionality in complex environments. As one of the recent techniques, the method proposed by Al-kaff et al (Al-Kaff, 2016) is to some extent capable of obtaining obstacle zone in complex environments and is one of the considerable researches in this field. This technique regards an obstacle as an object that is resizing in consecutive frames.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Vision Based Obstacle Detection in Uav Imaging

Badrloo

Varshosaz

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: Obstacle Detection, vision-based, mono-based, brain-inspired, dist-ratio ABSTRACT:Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detec tion techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, visionbased methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

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Section: Resultsmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Vision Based Obstacle Detection in Uav Imaging

Badrloo

Varshosaz

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Object traversing by monocular UAV in outdoor environment

Zhang

et al. 2020

Asian Journal of Control

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In search and monitoring tasks, object traversing scenarios are more challenging than avoidance and tracking. In this paper, we propose an object perception and path planning algorithm which can detect hollow objects and calculate a traversing path in real‐time. Meanwhile, a general vision unmanned aerial vehicle (UAV) system framework is designed to implement the proposed algorithm. A series of fundamental but effective algorithms are utilized in combination to detect hollow objects, and then the triangulation is applied to calculate the 3D position of the object. In order to improve the detection accuracy, a path planning and motion control algorithm is designed to enhance the safety and stability during traversing. The actual experimental results show that the UAV system is robust and feasible.

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Landmark-Based Virtual Path Estimation for Assisted UAV FPV Tele-Operation with Augmented Reality

Grijalva

Aguilar

2019

Intelligent Robotics and Applications

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Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

Cited by 45 publications

References 23 publications

Vision Based Obstacle Detection in Uav Imaging

Vision Based Obstacle Detection in Uav Imaging

Object traversing by monocular UAV in outdoor environment

Landmark-Based Virtual Path Estimation for Assisted UAV FPV Tele-Operation with Augmented Reality

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