Real-time stereo vision for collision detection on autonomous UAVs

Stanoev, Aleksandar; Audinet, Nicolas; Tancock, Scott; Dahnoun, Naim

doi:10.1109/ist.2017.8261524

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…In general terms, a stereo camera has two or more image sensors to simulate human binocular vision, giving it the ability to perceive depth, unlike a monocular camera. Both stereo and monocular cameras are able to perform object detection, but only stereo cameras are able to calculate the distance to an object with high accuracy [32].…”

Section: Rq2 How Many Cameras Did the Algorithms Need?mentioning

confidence: 99%

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Vera-Yanez,

Pereira,

Rodrigues

et al. 2023

J. Imaging

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This paper presents a systematic review of articles on computer-vision-based flying obstacle detection with a focus on midair collision avoidance. Publications from the beginning until 2022 were searched in Scopus, IEEE, ACM, MDPI, and Web of Science databases. From the initial 647 publications obtained, 85 were finally selected and examined. The results show an increasing interest in this topic, especially in relation to object detection and tracking. Our study hypothesizes that the widespread access to commercial drones, the improvements in single-board computers, and their compatibility with computer vision libraries have contributed to the increase in the number of publications. The review also shows that the proposed algorithms are mainly tested using simulation software and flight simulators, and only 26 papers report testing with physical flying vehicles. This systematic review highlights other gaps to be addressed in future work. Several identified challenges are related to increasing the success rate of threat detection and testing solutions in complex scenarios.

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Section: Rq2 How Many Cameras Did the Algorithms Need?mentioning

confidence: 99%

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Vera-Yanez,

Pereira,

Rodrigues

et al. 2023

J. Imaging

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“…Nevertheless, due to the information relevancy about the object's morphology, a vision technology becomes the most popular approach in safe HRC. In general, there are two kinds of vision systems used in safe HRC: single camera [19], [20] and stereo vision [7], [21]- [23]. While the standalone, however, the more expensive and demanding stereo vision technology allows estimating the 3D coordinates of the detected object.…”

Section: A Technologies and Sensorsmentioning

confidence: 99%

A Distributed Computing Real-Time Safety System of Collaborative Robot

Gradolewski

Masłowski²,

Dziak

et al. 2020

ELEKTRON ELEKTROTECH

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Robotization has become common in modern factories due to its efficiency and cost-effectiveness. Lots of robots and manipulators share their workspaces with humans what could lead to hazardous situations causing health damage or even death. This article presents a real-time safety system applying the distributed computing paradigm for a collaborative robot. The system consists of detection/sensing modules connected with a server working as decision-making system. Each configurable sensing module pre-processes vision information and then sends to the server the images cropped to new objects extracted from a background. After identifying persons from the images, the decision-making system sends a request to the robot to perform pre-defined action. In the proposed solution, there are indicated three safety zones defined by three different actions on a robot motion. As identification method, state-of-the-art of Machine Learning algorithms, the Histogram of Oriented Gradients (HOG), Viola-Jones, and You Only Look Once (YOLO), have been examined and presented. The industrial environment tests indicated that YOLOv3 algorithm outperformed other solutions in terms of identification capabilities, false positive rate and maximum latency.

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“…Regarding algorithms for obstacle avoidance, for instance A. Stanoev et al [9] establish a threshold in the depth map where the close objects are white and labeled as obstacles and the farther ones are black and then ignored; if the robot moves quickly, the threshold decreases. In some cases it is necessary to differentiate obstacles over a flat surface, in this case it is useful to implement V-disparity maps [10] which is a function of the disparity map, that accumulates the disparities of the horizontal line into the v-disparity function, where the abscissa corresponds to the number of disparities.…”

Section: A Previous Workmentioning

confidence: 99%

Depth map estimation methodology for detecting free-obstacle navigation areas

Trejo

Martinez

Flores

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a vision-based methodology which makes use of a stereo camera rig and a one dimension LiDAR to estimate free obstacle areas for quadrotor navigation. The presented approach fuses information provided by a depth map from a stereo camera rig, and the sensing distance of the 1D-LiDAR. Once the depth map is filtered with a Weighted Least Squares filter (WLS), the information is fused through a Kalman filter algorithm. To determine if there is a free space large enough for the quadrotor to pass through, our approach marks an area inside the disparity map by using the Kalman Filter output information. The whole process is implemented in an embedded computer Jetson TX2 and coded in the Robotic Operating System (ROS). Experiments demonstrate the effectiveness of our approach.

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Real-time stereo vision for collision detection on autonomous UAVs

Cited by 7 publications

References 13 publications

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

A Distributed Computing Real-Time Safety System of Collaborative Robot

Depth map estimation methodology for detecting free-obstacle navigation areas

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