Application of Augmented Reality and Robotic Technology in Broadcasting: A Survey

Yan, Dingtian; Hu, Huosheng

doi:10.3390/robotics6030018

Cited by 19 publications

(9 citation statements)

References 56 publications

(68 reference statements)

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“…Another factor that drops performance in dynamically changing environments is occlusion of markers which was noted in our previous work [10]. On this account, marker-less approaches have became a major focus [17]. This includes model-based approaches which use foreknown 3D models to localize objects and generate the pose estimation for the AR device.…”

Section: A Ar Calibration Methodsmentioning

confidence: 99%

“…With the recent advances in machine learning algorithms, especially deep learning, and the higher computational capabilities of computers, networks can be trained to recognize 3D objects. An overview of current research is provided by Yan et al [17]. Recent publications by [19], [20] have focused on learning directly from 3D data.…”

Section: A Ar Calibration Methodsmentioning

confidence: 99%

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A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data

Kästner¹,

Dimitrov²,

Lambrecht³

2020

Preprint

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Augmented Reality has been subject to various integration efforts within industries due to its ability to enhance human machine interaction and understanding. Neural networks have achieved remarkable results in areas of computer vision, which bear great potential to assist and facilitate an enhanced Augmented Reality experience. However, most neural networks are computationally intensive and demand huge processing power thus, are not suitable for deployment on Augmented Reality devices. In this work we propose a method to deploy state of the art neural networks for real time 3D object localization on augmented reality devices. As a result, we provide a more automated method of calibrating the AR devices with mobile robotic systems. To accelerate the calibration process and enhance user experience, we focus on fast 2D detection approaches which are extracting the 3D pose of the object fast and accurately by using only 2D input. The results are implemented into an Augmented Reality application for intuitive robot control and sensor data visualization. For the 6D annotation of 2D images, we developed an annotation tool, which is, to our knowledge, the first open source tool to be available. We achieve feasible results which are generally applicable to any AR device thus making this work promising for further research in combining high demanding neural networks with Internet of Things devices.

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Section: A Ar Calibration Methodsmentioning

confidence: 99%

Section: A Ar Calibration Methodsmentioning

confidence: 99%

A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data

Kästner¹,

Dimitrov²,

Lambrecht³

2020

Preprint

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“…Technologies, such as cameras, infrared sensors, hybrid sensors, and 2D and 3D markers, can all be implemented to identify a pattern and track its position in the real world. Robotic cameraman systems have been proposed to increase the quality of broadcasting systems and replace human operators [40]. It has been shown that robotic cameramen facilitate more precise and advanced interaction with virtual elements, and through zooming and multiple angle views, improve the performance of AR broadcasting in all sorts of environments.…”

Section: Ar In Entertainmentmentioning

confidence: 99%

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

Parekh

Patel

et al. 2020

Vis. Comput. Ind. Biomed. Art

116

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This paper presents a detailed review of the applications of augmented reality (AR) in three important fields where AR use is currently increasing. The objective of this study is to highlight how AR improves and enhances the user experience in entertainment, medicine, and retail. The authors briefly introduce the topic of AR and discuss its differences from virtual reality. They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience. The growth of AR in markets is also briefly discussed. In the three sections of the paper, the applications of AR are discussed. The use of AR in multiplayer gaming, computer games, broadcasting, and multimedia videos, as an aspect of entertainment and gaming is highlighted. AR in medicine involves the use of AR in medical healing, medical training, medical teaching, surgery, and post-medical treatment. AR in retail was discussed in terms of its uses in advertisement, marketing, fashion retail, and online shopping. The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.

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“…Barcode has also been used in combination with AR for commercial purposes [17]. AR had also been used for broadcasting enhancement as mentioned by Yan and Hu [18] which in general depends on AR display, AR tracking, and robotic AR broadcast.…”

Section: State Of the Artmentioning

confidence: 99%

An Education Application for Teaching Robot Arm Manipulator Concepts Using Augmented Reality

Hernández-Ordoñez

Calles-Arriaga²,

Montaño-Rivas

et al. 2018

Mobile Information Systems

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Teaching robotics is a challenge in many universities due to the mathematics concepts used in this area. In recent years, augmented reality has improved learning in several engineering areas. In this paper, a platform for teaching robotic arm manipulation concepts is presented. e system includes a homemade robotic arm, a control system, and the RAR@pp. e RAR@pp is focused on learning robotic arm manipulation algorithms by the detection of markers in the robotic arm and displaying in real time the values based on the data obtained by the control system. Details on the design of the platform are presented, and the related results are discussed. Experimental data about the usability of the application are also shown.

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Application of Augmented Reality and Robotic Technology in Broadcasting: A Survey

Cited by 19 publications

References 56 publications

A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data

A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

An Education Application for Teaching Robot Arm Manipulator Concepts Using Augmented Reality

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