Obstacle size and proximity detection using stereo images for agile aerial robots

Majumder, Shibarchi; Shankar, Rahul; Prasad, Mani Shankar

doi:10.1109/spin.2015.7095261

Cited by 5 publications

(4 citation statements)

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“…A technique based on stereo cameras; in order to estimate the proximity of the obstacles, was introduced in Majumder, Shankar, and Prasad (2015) . Based on the disparity images and the view angle, the system detects the size and the position of the obstacles, and calculates the relation of the size and its distance to the UAV.…”

Section: Visual Obstacle Detection and Avoidancementioning

confidence: 99%

“…( Majumder et al, 2015 ) Monocular ( Al-Kaff,Meng, Martín, de la Escalera, & Armingol, 2016; Bills et al, 2011; Ma et al, 2015; Saha et al, 2014 ) ( Lenz, Gemici, & Saxena, 2012; Mori & Scherer, 2013 ) Lee et al, 2012; Lyu et al, 2015; Neff, Lee, Chitrakaran, Dawson, & Burg, 2007; Olivares-Mendez et al, 2015 ) ( Bošnak, Matko, & Blaži č, 2012; Kurnaz, Cetin, & Kaynak, 2010; Shang, Liu, Zhao, & Chen, 2016; Zhang, Fang, Liang, & Zhang, 2016 )…”

mentioning

confidence: 99%

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Survey of computer vision algorithms and applications for unmanned aerial vehicles

Al-Kaff

Martín

García

et al. 2018

Expert Systems with Applications

186

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Section: Visual Obstacle Detection and Avoidancementioning

confidence: 99%

mentioning

confidence: 99%

Survey of computer vision algorithms and applications for unmanned aerial vehicles

Al-Kaff

Martín

García

et al. 2018

Expert Systems with Applications

186

View full text Add to dashboard Cite

“…In addition, the judgement towards the obstacle with respect to its estimated distance and bearing will also be negatively affected (real time effect). In order to work with depth estimation of the environment, researchers tend to employ the stereo vision sensor to relatively estimate the distance of the observed environment from the UAV, which includes the obstacles [16,17]. However, the major problem for the stereo vision sensor is the length constraints of the baseline for both of the attached camera sensors, which in turn can decrease the accuracy of the estimation.…”

Section: Related Workmentioning

confidence: 99%

Sudden Obstacle Appearance Detection by Analyzing Flow Field Vector for Small-Sized UAV

Ramli

Shamsudin²,

Yaakub³

2021

PAAT

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Achieving a reliable obstacle detection and avoidance system that can provide an effective safe avoidance path for small unmanned aerial vehicle (UAV) is very challenging due to its physical size and weight constraints. Prior works tend to employ the vision based-sensor as the main detection sensor but resulting to high dependency on texture appearance while not having a distance sensing capabilities. The previous system only focused on the detection of the static frontal obstacle without observing the environment which may have moving obstacles. On the other hand, most of the wide spectrum range sensors are heavy and expensive hence not suitable for small UAV. In this work, integration of different based sensors was proposed for a small UAV in detecting unpredictable obstacle appearance situation. The detection of the obstacle is accomplished by analysing the flow field vectors in the image frames sequence. The proposed system was evaluated by conducting the experiments in a real environment which consisted of different configuration of the obstacles. The results from the experiment show that the success rate for detecting unpredictable obstacle appearance is high which is 70% and above. Even though some of the introduced obstacles are considered to have poor texture appearances on their surface, the proposed obstacle detection system was still able to detect the correct appearance movement of the obstacles by detecting the edges.

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“…Other works calculate the depth (distance) of the obstacles, such as in [ 16 , 17 ]. A technique based on stereo cameras, in order to estimate the proximity of the obstacles, was introduced in [ 18 ]. At which, the system detects the size and the position of the obstacles based on the disparity images and the view angle.…”

Section: Introductionmentioning

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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Obstacle size and proximity detection using stereo images for agile aerial robots

Cited by 5 publications

References 1 publication

Survey of computer vision algorithms and applications for unmanned aerial vehicles

Survey of computer vision algorithms and applications for unmanned aerial vehicles

Sudden Obstacle Appearance Detection by Analyzing Flow Field Vector for Small-Sized UAV

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

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