Relevant bibliographies by topics / ArUco-Marker

Contents

  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 (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, et al. "Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition." Electronics 8, no. 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
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Botta, Andrea, and Giuseppe Quaglia. "Performance Analysis of Low-Cost Tracking System for Mobile Robots." Machines 8, no. 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 or
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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 (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 t
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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 (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 th
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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 (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 analytica
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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 фокусн
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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-dimension
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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
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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 Py
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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. 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. 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”. 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
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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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