Relevant bibliographies by topics / ArUco-Marker

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'ArUco-Marker'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "ArUco-Marker"

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Tocci, Tommaso, Lorenzo Capponi, and Gianluca Rossi. "ArUco marker-based displacement measurement technique: uncertainty analysis." Engineering Research Express 3, no. 3 (August 30, 2021): 035032. http://dx.doi.org/10.1088/2631-8695/ac1fc7.

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UKIGAI, Yuuki, Syuuhei SANTOKI, Atsushi FUJIMORI, and Shinsuke OH-HARA. "Formation control between quadrotor and mobile robot using an aruco marker." Proceedings of Yamanashi District Conference 2018 (2018): YC2018–009. http://dx.doi.org/10.1299/jsmeyamanashi.2018.yc2018-009.

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Wubben, Jamie, Francisco Fabra, Carlos T. Calafate, Tomasz Krzeszowski, Johann M. Marquez-Barja, Juan-Carlos Cano, and Pietro Manzoni. "Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition." Electronics 8, no. 12 (December 12, 2019): 1532. http://dx.doi.org/10.3390/electronics8121532.

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Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56 × 56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.
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Botta, Andrea, and Giuseppe Quaglia. "Performance Analysis of Low-Cost Tracking System for Mobile Robots." Machines 8, no. 2 (June 2, 2020): 29. http://dx.doi.org/10.3390/machines8020029.

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This paper proposes a reliable and straightforward approach to mobile robots (or moving objects in general) indoor tracking, in order to perform a preliminary study on their dynamics. The main features of this approach are its minimal and low-cost setup and a user-friendly interpretation of the data generated by the ArUco library. By using a commonly available camera, such as a smartphone one or a webcam, and at least one marker for each object that has to be tracked, it is possible to estimate the pose of these markers, with respect to a reference conveniently placed in the environment, in order to produce results that are easily interpretable by a user. This paper presents a simple extension to the ArUco library to generate such user-friendly data, and it provides a performance analysis of this application with static and moving objects, using a smartphone camera to highlight the most notable feature of this solution, but also its limitations.
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Zakiev, Aufar, Ksenia Shabalina, and Evgeni Magid. "Pilot Virtual Experiments on ArUco and AprilTag Systems Comparison for Fiducial Marker Rotation Resistance." Proceedings of International Conference on Artificial Life and Robotics 24 (January 10, 2019): 132–35. http://dx.doi.org/10.5954/icarob.2019.os4-7.

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Yu, Jing, Wensong Jiang, Zai Luo, and Li Yang. "Application of a Vision-Based Single Target on Robot Positioning System." Sensors 21, no. 5 (March 5, 2021): 1829. http://dx.doi.org/10.3390/s21051829.

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In this paper, we propose a Circular-ring visual location marker based on a global image-matching model to improve the positioning ability in the fiducial marker system of a single-target mobile robot. The unique coding information is designed according to the cross-ratio invariance of the projective theorem. To verify the accuracy of full 6D pose estimation using the Circular-ring marker, a 6 degree of freedom (DoF) robotic arm platform is used to design a visual location experiment. The experimental result shows in terms of small resolution images, different size markers, and long-distance tests that our proposed robot positioning method significantly outperforms AprilTag, ArUco, and Checkerboard. Furthermore, through a repeatable robot positioning experiment, the results indicated that the proposed Circular-ring marker is twice as accurate as the fiducial marker at 2–4 m. In terms of recognition speed, the Circular-ring marker processes a frame within 0.077 s. When the Circular-ring marker is used for robot positioning at 2–4 m, the maximum average translation error of the Circular-ring marker is 2.19, 3.04, and 9.44 mm. The maximum average rotation error is also 1.703°, 1.468°, and 0.782°.
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Aalerud, Atle, Joacim Dybedal, and Geir Hovland. "Automatic Calibration of an Industrial RGB-D Camera Network Using Retroreflective Fiducial Markers." Sensors 19, no. 7 (March 31, 2019): 1561. http://dx.doi.org/10.3390/s19071561.

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This paper describes a non-invasive, automatic, and robust method for calibrating a scalable RGB-D sensor network based on retroreflective ArUco markers and the iterative closest point (ICP) scheme. We demonstrate the system by calibrating a sensor network comprised of six sensor nodes positioned in a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. Here, the automatic calibration achieved an average Euclidean error of 3 cm at distances up to 9.45 m. To achieve robustness, we apply several innovative techniques: Firstly, we mitigate the ambiguity problem that occurs when detecting a marker at long range or low resolution by comparing the camera projection with depth data. Secondly, we use retroreflective fiducial markers in the RGB-D calibration for improved accuracy and detectability. Finally, the repeating ICP refinement uses an exact region of interest such that we employ the precise depth measurements of the retroreflective surfaces only. The complete calibration software and a recorded dataset are publically available and open source.
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Bi, Shusheng, Dongsheng Yang, and Yueri Cai. "Automatic Calibration of Odometry and Robot Extrinsic Parameters Using Multi-Composite-Targets for a Differential-Drive Robot with a Camera." Sensors 18, no. 9 (September 14, 2018): 3097. http://dx.doi.org/10.3390/s18093097.

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This paper simultaneously calibrates odometry parameters and the relative pose between a monocular camera and a robot automatically. Most camera pose estimation methods use natural features or artificial landmark tools. However, there are mismatches and scale ambiguity for natural features; the large-scale precision landmark tool is also challenging to make. To solve these problems, we propose an automatic process to combine multiple composite targets, select keyframes, and estimate keyframe poses. The composite target consists of an aruco marker and a checkerboard pattern. First, an analytical method is applied to obtain initial values of all calibration parameters; prior knowledge of the calibration parameters is not required. Then, two optimization steps are used to refine the calibration parameters. Planar motion constraints of the camera are introduced in these optimizations. The proposed solution is automatic; manual selection of keyframes, initial values, and robot construction within a specific trajectory are not required. The competing accuracy and stability of the proposed method under different target placements and robot paths are tested experimentally. Positive effects on calibration accuracy and stability are obtained when (1) composite targets are adopted; (2) two optimization steps are used; (3) plane motion constraints are introduced; and (4) target numbers are increased.
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Бурыкин, Ю. Г. "A Video Tracking System for the Registration of Upper Extremity Motions." Успехи кибернетики / Russian Journal of Cybernetics, no. 3(3) (September 30, 2020): 23–32. http://dx.doi.org/10.51790/2712-9942-2020-1-3-3.

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Автором рассмотрены основные способы регистрации движений верхней конечности человека с помощью различных технических средств, описаны недостатки системы видеоанализа и предложены альтернативные варианты решения ряда задач, направленных на уменьшение потери информации при видеорегистрации. Регистрация пальцевого тремора и движений пальцев осуществлялась посредством видеокамеры на основе системы распознавания образов, путем идентификации 5 меток (маркеров), сгенерированных с помощью свободной библиотеки ArUco. В результате оптимизации параметров маркеров, а также настройки углов обзора и фокусного расстояния удалось снизить ошибки при распознавании образов и повысить надежность регистрации движений фаланг пальцев с помощью видеокамеры. Использование калибровочных фотографий, полученных с экрана монитора, расположенного в различных плоскостях, позволило повысить точность регистрации движений. Информация о параметрах двигательной активности человека актуальна для объективной оценки его психофизиологического состояния и координационных способностей, а также медицинской диагностики. We considered the key methods for registering human upper extremity motions with various SW/HW tools, presented the drawbacks of video analysis systems, and proposed alternative solutions intended for reducing video information losses. We registered finger tremor/movement with a video camera and a pattern recognition system by identifying 5 markers generated with the AtUco open source library. By optimizing the marker properties, the view angles and the focal length we managed to reduce pattern recognition errors and improve phalanx movement video registration quality. Using reference photos taken from the monitor positioned at various angles also improved the motion registration quality. The human motion information is relevant to objectively assess the person’s psychophysiological status and physical coordination. It is also used for medical diagnostics.
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Poroykov, Anton, Pavel Kalugin, Sergey Shitov, and Irina Lapitskaya. "Modeling ArUco Markers Images for Accuracy Analysis of Their 3D Pose Estimation." Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2, December 17, 2020, short14–1—short14–7. http://dx.doi.org/10.51130/graphicon-2020-2-4-14.

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Fiducial markers are used in vision systems to determine the position of objects in space, reconstruct movement and create augmented reality. Despite the abundance of work on analysis of the accuracy of the estimation of the fiducial markers spatial position, this question remains open. In this paper, we propose the computer modeling of images with ArUco markers for this purpose. The paper presents a modeling algorithm, which was implemented in the form of software based on the OpenCV library. Algorithm is based on projection of three-dimensional points of the marker corners into two-dimensional points using the camera parameters and rendering the marker image in the new two-dimensional coordinates on the modeled image with the use of the perspective transformation obtained from these points. A number of dependencies were obtained by which it is possible to evaluate the error in determining the position depending on markers size. Including the probability of detecting a marker depending on its area on an image.
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Dissertations / Theses on the topic "ArUco-Marker"

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Bilda, Sebastian. "Optische Methoden zur Positionsbestimmung auf Basis von Landmarken." Master's thesis, Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-226934.

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Die Innenraumpositionierung kommt in der heutigen Zeit immer mehr Aufmerksamkeit zu teil. Neben der Navigation durch das Gebäude sind vor allem Location Based Services von Bedeutung, welche Zusatzinformationen zu spezifischen Objekten zur Verfügung stellen Da für eine Innenraumortung das GPS Signal jedoch zu schwach ist, müssen andere Techniken zur Lokalisierung gefunden werden. Neben der häufig verwendeten Positionierung durch Auswertung von empfangenen Funkwellen existieren Methoden zur optischen Lokalisierung mittels Landmarken. Das kamerabasierte Verfahren bietet den Vorteil, dass eine oft zentimetergenaue Positionierung möglich ist. In dieser Masterarbeit erfolgt die Bestimmung der Position im Gebäude mittels Detektion von ArUco-Markern und Türschildern aus Bilddaten. Als Evaluationsgeräte sind zum einen die Kinect v2 von Microsoft, als auch das Lenovo Phab 2 Pro Smartphone verwendet worden. Neben den Bilddaten stellen diese auch mittels Time of Flight Sensoren generierte Tiefendaten zur Verfügung. Durch den Vergleich von aus dem Bild extrahierten Eckpunkten der Landmarke, mit den aus einer Datenbank entnommenen realen geometrischen Maßen des Objektes, kann die Entfernung zu einer gefundenen Landmarke bestimmt werden. Neben der optischen Distanzermittlung wird die Position zusätzlich anhand der Tiefendaten ermittelt. Abschließend werden beiden Verfahren miteinander verglichen und eine Aussage bezüglich der Genauigkeit und Zuverlässigkeit des in dieser Arbeit entwickelten Algorithmus getroffen
Indoor Positioning is receiving more and more attention nowadays. Beside the navigation through a building, Location Bases Services offer the possibility to get more information about certain objects in the enviroment. Because GPS signals are too weak to penetrate buildings, other techniques for localization must be found. Beneath the commonly used positioning via the evaluation of received radio signals, optical methods for localization with the help of landmarks can be used. These camera-based procedures have the advantage, that an inch-perfect positioning is possible. In this master thesis, the determination of the position in a building is chieved through the detection of ArUco-Marker and door signs in images gathered by a camera. The evaluation is done with the Microsoft Kinect v2 and the Lenovo Phab 2 Pro Smartphone. They offer depth data gained by a time of flight sensor beside the color images. The range to a detected landmark is calculated by comparing the object´s corners in the image with the real metrics, extracted from a database. Additionally, the distance is determined by the evaluation of the depth data. Finally, both procedures are compared with each other and a statement about the accuracy and responsibility is made
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Karlsson, Christoffer. "Vision based control and landing of Micro aerial vehicles." Thesis, Karlstads universitet, Avdelningen för fysik och elektroteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73225.

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This bachelors thesis presents a vision based control system for the quadrotor aerial vehicle,Crazy ie 2.0, developed by Bitcraze AB. The main goal of this thesis is to design andimplement an o-board control system based on visual input, in order to control the positionand orientation of the vehicle with respect to a single ducial marker. By integrating a cameraand wireless video transmitter onto the MAV platform, we are able to achieve autonomousnavigation and landing in relatively close proximity to the dedicated target location.The control system was developed in the programming language Python and all processing ofthe vision-data take place on an o-board computer. This thesis describes the methods usedfor developing and implementing the control system and a number of experiments have beencarried out in order to determine the performance of the overall vision control system. Withthe proposed method of using ducial markers for calculating the control demands for thequadrotor, we are able to achieve autonomous targeted landing within a radius of 10centimetres away from the target location.
I detta examensarbete presenteras ett visionsbaserat kontrollsystem for dronaren Crazy ie 2.0som har utvecklats av Bitcraze AB. Malet med detta arbete ar att utforma och implementeraett externt kontrollsystem baserat pa data som inhamtas av en kamera for att reglera fordonetsposition och riktning med avseende pa en markor placerad i synfaltet av kameran. Genom attintegrera kameran tillsammans med en tradlos videosandare pa plattformen, visar vi i dennaavhandling att det ar mojligt att astadkomma autonom navigering och landning i narheten avmarkoren.Kontrollsystemet utvecklades i programmeringsspraket Python och all processering avvisions-datan sker pa en extern dator. Metoderna som anvands for att utvecklakontrollsystemet och som beskrivs i denna rapport har testats under ett ertal experiment somvisar pa hur val systemet kan detektera markoren och hur val de olika ingaendekomponenterna samspelar for att kunna utfora den autonoma styrningen. Genom den metodsom presenteras i den har rapporten for att berakna styrsignalerna till dronaren med hjalp avvisuell data, visar vi att det ar mojligt att astadkomma autonom styrning och landning motmalet inom en radie av 10 centimeter.
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Book chapters on the topic "ArUco-Marker"

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Lebedev, Igor, Aleksei Erashov, and Aleksandra Shabanova. "Accurate Autonomous UAV Landing Using Vision-Based Detection of ArUco-Marker." In Lecture Notes in Computer Science, 179–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60337-3_18.

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Koeda, Masanao, Daiki Yano, Naoki Shintaku, Katsuhiko Onishi, and Hiroshi Noborio. "Development of Wireless Surgical Knife Attachment with Proximity Indicators Using ArUco Marker." In Lecture Notes in Computer Science, 14–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91244-8_2.

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Zakiev, Aufar, Ksenia Shabalina, Tatyana Tsoy, and Evgeni Magid. "Pilot Virtual Experiments on ArUco and ArTag Systems Comparison for Fiducial Marker Rotation Resistance." In Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings”, 455–64. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9267-2_37.

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Conference papers on the topic "ArUco-Marker"

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Wang, Yutao, Zongpeng Zheng, Zhiqi Su, Gang Yang, Zhong Wang, and Yu Luo. "An Improved ArUco Marker for Monocular Vision Ranging." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164176.

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Li, Boxuan, Jiezhang Wu, Xiaojun Tan, and Benfei Wang. "ArUco Marker Detection under Occlusion Using Convolutional Neural Network." In 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE). IEEE, 2020. http://dx.doi.org/10.1109/cacre50138.2020.9230250.

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Kam, Ho Chuen, Ying Kin Yu, and Kin Hong Wong. "An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter." In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2018. http://dx.doi.org/10.1109/snpd.2018.8441049.

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Ren, Ranzhen, Lichuan Zhang, Yijie Yuan, Zhimo Wang, and Lu Liu. "Underwater Visual Tracking Method Based on KCF Algorithm of ArUco Marker." In Global Oceans 2020: Singapore - U.S. Gulf Coast. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf38699.2020.9389441.

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Meleško, Jaroslav. "ROBUST AUGMENTED REALITY SYSTEM." In Inžinerinė grafika ir projektavimas. Informacinių technologijų sauga ir informacinės sistemos. VGTU Technika, 2016. http://dx.doi.org/10.3846/itsis.2016.09.

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I developed an augmented reality system based on Hough transform to transcend limitations of popular Augmented Reality libraries such as ArUco. The purpose of the system is to project a digital image on to a real canvas, merging digital and traditional artistic media for ease of art production and practice. The technological challenges were identified in fiducial marker based prototype and overcome in the Hough transform based system are as follows: need for a printed marker, imprecision and weakness to occlusion. The developed software based on Hough transform works with any standard sheet of paper (A4, A3 formats). The system projects a photograph directly on to canvas or paper and automatically rotates and fits the image within the canvas. It is also resistant to occlusion, such as a hand on top of the canvas or canvas that is not fully within the frame. Resistance to small scale noise is achieved thought Hough transform and large occlusions are overcome by a line averaging algorithm. The software has shown 14% better performance than analogical ArUco library based software.
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Elangovan, Nathan, Anany Dwivedi, Lucas Gerez, Che-Ming Chang, and Minas Liarokapis. "Employing IMU and ArUco Marker Based Tracking to Decode the Contact Forces Exerted by Adaptive Hands." In 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids). IEEE, 2019. http://dx.doi.org/10.1109/humanoids43949.2019.9035051.

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Sani, Mohammad Fattahi, and Ghader Karimian. "Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors." In 2017 International Conference on Computer and Drone Applications (IConDA). IEEE, 2017. http://dx.doi.org/10.1109/iconda.2017.8270408.

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Zakiev, Aufar, Tatyana Tsoy, Ksenia Shabalina, Evgeni Magid, and Subir Kumar Saha. "Virtual Experiments on ArUco and AprilTag Systems Comparison for Fiducial Marker Rotation Resistance under Noisy Sensory Data." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207701.

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