Accurate Autonomous UAV Landing Using Vision-Based Detection of ArUco-Marker

Lebedev, Igor; Erashov, Aleksei; Shabanova, Aleksandra

doi:10.1007/978-3-030-60337-3_18

Cited by 21 publications

(11 citation statements)

References 18 publications

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“…In the first category of approaches, markers are detected based on their appearance or geometry using traditional image features, and then the relative pose of the UAV is computed from these extracted feature points. Over the years, several types of markers have been proposed for this purpose including point markers [3,4], circle markers [5], H-shaped markers [6,7], square markers [8] and ArUco markers [9,13]. These approaches require the landing site to be predestined and are not employable in unstructured or unfamiliar environments.…”

Section: Landing On a Known Areamentioning

confidence: 99%

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Landing Site Detection for Autonomous Rotor Wing UAVs Using Visual and Structural Information

Chatzikalymnios

Μουστάκας

2022

J Intell Robot Syst

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The technology of unmanned aerial vehicles (UAVs) has increasingly become part of many civil and research applications in recent years. UAVs offer high-quality aerial imaging and the ability to perform quick, flexible and in-depth data acquisition over an area of interest. While navigating in remote environments, UAVs need to be capable of autonomously landing on complex terrains for security, safety and delivery reasons. This is extremely challenging as the structure of these terrains is often unknown, and no prior knowledge can be leveraged. In this study, we present a vision-based autonomous landing system for rotor wing UAVs equipped with a stereo camera and an inertial measurement unit (IMU). The landing site detection algorithm introduces and evaluates several factors including terrain’s flatness, inclination and steepness. Considering these features we compute map metrics that are used to obtain a landing-score map, based on which we detect candidate landing sites. The 3D reconstruction of the scene is acquired by stereo processing and the pose of the UAV at any given time is estimated by fusing raw data from the inertial sensors with the pose obtained from stereo ORB-SLAM2. Real-world trials demonstrate successful landing in unknown and complex terrains such as suburban and forest areas.

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Section: Landing On a Known Areamentioning

confidence: 99%

“…Lebedev et al [13] presents a combination of two algorithms for on-image search and accurate landing on an ArUco marker. When the marker appears in the acquired image, it's characteristics are revealed and the algorithm estimates the orientation and position of the marker and the distance from the observing vehicle.…”

Section: Landing On a Known Areamentioning

confidence: 99%

Landing Site Detection for Autonomous Rotor Wing UAVs Using Visual and Structural Information

Chatzikalymnios

Μουστάκας

2022

J Intell Robot Syst

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“…Regarding pattern recognition landing systems, works such as [13,14], among others [15][16][17][18], focus on finding the position using known patterns by Perspective-n-Point (PnP) algorithms [19]. Patterns such as Aruco [20], charuco, or new fractal patterns such as [21] or the deep learning trend You Only Look Once (YOLO) [22] try to improve the pattern pose estimation and prove to be widespread systems in the literature.…”

Section: Introductionmentioning

confidence: 99%

Error Reduction in Vision-Based Multirotor Landing System

Caña

García

Molina

2022

Sensors

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New applications are continuously appearing with drones as protagonists, but all of them share an essential critical maneuver—landing. New application requirements have led the study of novel landing strategies, in which vision systems have played and continue to play a key role. Generally, the new applications use the control and navigation systems embedded in the aircraft. However, the internal dynamics of these systems, initially focused on other tasks such as the smoothing trajectories between different waypoints, can trigger undesired behaviors. In this paper, we propose a landing system based on monocular vision and navigation information to estimate the helipad global position. In addition, the global estimation system includes a position error correction module by cylinder space transformation and a filtering system with a sliding window. To conclude, the landing system is evaluated with three quality metrics, showing how the proposed correction system together with stationary filtering improves the raw landing system.

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“…When virtual markers are not available, physical markers are employed [22], [23], and among them, the ArUco marker library (ArUco -Augmented Reality University of Cordoba) was found to be one of the most effective and robust to detection errors and occlusion [24]- [26]. Elangovan et al [27] used them for decoding contact forces exerted by adaptive hands, while Sani and Karamian [28] and Lebedev et al [29] employed them for drone quadrotor and UAV autonomous navigation and landing, respectively. In relation to the use of fiducial markers for vibrations measurement, Abdelbarr et al [30] researched structural 3D displacement using ArUco markers, while the study of Kalybek et al [31] provides one of the first evidence of the capability of optical vibration monitoring systems in modal identifications.…”

Section: Introductionmentioning

confidence: 99%

Frequency response function identification using fused filament fabrication-3D-printed embedded ArUco markers

Capponi

Tocci²,

Tribbiani³

et al. 2022

ACTA IMEKO

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The assessment of modal components is a fundamental step in structural dynamics. While experimental investigations are generally performed through full-contact techniques, using accelerometers or modal hammers, this research proposes a non-contact Frequency Response Function identification measurement technique based on ArUco markers displacement detection. The proposed method is presented using a harmonically excited FFF 3D-printed flexible structure, equipped with multiple embedded-printed markers, whose displacement is measured with an industrial camera. Comparison with numerical simulation and an established experimental approach is finally provided for the results validation.

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Accurate Autonomous UAV Landing Using Vision-Based Detection of ArUco-Marker

Cited by 21 publications

References 18 publications

Landing Site Detection for Autonomous Rotor Wing UAVs Using Visual and Structural Information

Landing Site Detection for Autonomous Rotor Wing UAVs Using Visual and Structural Information

Error Reduction in Vision-Based Multirotor Landing System

Frequency response function identification using fused filament fabrication-3D-printed embedded ArUco markers

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