Embedded ArUco: a novel approach for high precision UAV landing

Khazetdinov, Artur; Zakiev, Aufar; Tsoy, Tatyana; Magid, Evgeni

doi:10.1109/sibcon50419.2021.9438855

Cited by 27 publications

(6 citation statements)

References 28 publications

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“…Specifically, the drone is assigned the task of reaching specific points within the space. Aruco environmental markers [21][22][23], in conjunction with the associated OpenCV libraries, are used for this purpose.…”

Section: Scenariomentioning

confidence: 99%

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Gelli,

Bigazzi,

Boni

et al. 2024

Drones

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In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.

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Section: Scenariomentioning

confidence: 99%

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Gelli,

Bigazzi,

Boni

et al. 2024

Drones

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show abstract

“…Despite the several improvements in and works on the area of fiducial marker and landing algorithms to assist the UAS [48][49][50][51][52][53][54], some challenges still need to be addressed for the visual servoing controller. For instance, the authors of Acuna and Willet [55] dealt with the limit distance issue to detect the fiducial marker by proposing a control scheme to dynamically change the appearance of the marker to increase the range of detection.…”

Section: Uas Landing Strategiesmentioning

confidence: 99%

“…However, they do not address the landing algorithm. Khazetdinov et al [48] developed a new type of fiducial marker called embedded ArUco (e-ArUco). The authors performed the tests with the developed marker and the landing algorithm within the Gazebo simulator using the ROS framework.…”

Section: Uas Landing Strategiesmentioning

confidence: 99%

Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario

et al. 2023

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The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

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“…Various artificial markers have been proposed for ease of detectability [11][12][13][14], such as L-type, arrow-type, dot-array, ArUco and chessboard markers. For example, Spinczyk et al [15] used a chessboard to track respiration motion.…”

Section: Introductionmentioning

confidence: 99%

Robust 3D Object Tracking with X-Triplet Markers: A Machine Learning-Based Approach

Shen

Cai

Deng

et al. 2023

Preprint

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In this paper, we propose an efficient and robust X-Triplet detection method based on the support vector machine (SVM) and an adjacent matrix for locating and tracking objects through stereo vision with minimal feature points. The X-Triplet, denoted as Tri-X, is a composite marker consisting of three sequential X-corners. The definition and types of Tri-X markers are initially presented. Further, a fast and robust X-corner detector based on the block search strategy and SVM is proposed for extracting X-corner candidates along with their sub-pixel locations and orientations. The X-corner adjacent matrix (XAM) is then constructed using the orientation angle error to describe the possibility that any X-corner pair can form a valid edge vector. The Tri-X candidates are extracted efficiently from the XAM. Once the Tri-X markers are detected in binocular images, their 6D pose information can be recovered through stereo matching and triangulation technique. When multiple targets are involved simultaneously, different Tri-X markers can be utilized to identify different objects. Experimental results demonstrate the superiority of our method in terms of both accuracy and efficiency for X-corner and Tri-X marker detection, surpassing state-of-the-art approaches. Notably, our method achieves exceptional localization precision with a remarkable position error below 0.1mm and an orientation error below 1 degree. Additionally, our method exhibits great potential for utilization in user-defined specific tracking tasks, offering flexibility and adaptability to various tracking requirements.

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Embedded ArUco: a novel approach for high precision UAV landing

Cited by 27 publications

References 28 publications

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario

Robust 3D Object Tracking with X-Triplet Markers: A Machine Learning-Based Approach

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