Catching the Drone - A Tangible Augmented Reality Game in Superhuman Sports

Eichhorn, Christian; Jadid, Adnane; Plecher, David A.; Weber, Sandro; Klinker, Gudrun; Itoh, Yuta

doi:10.1109/ismar-adjunct51615.2020.00022

Cited by 14 publications

(9 citation statements)

References 26 publications

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“…Another trend evolves around the idea of utilizing AR for sports to augment the human body and allowing the user to receive superhuman abilities. The Superhuman Sports genre provided game concepts such as Catching the Drone (Eichhorn et al, 2020) or League of Lasers (Miedema et al, 2019). Early research in the area of collaborative AR has provided a rich foundation for today's diversification of concepts (Schmalstieg et al, 2002).…”

Section: Related Workmentioning

confidence: 99%

Cloud-based cross-platform collaborative augmented reality in flutter

Carius¹,

Eichhorn²,

Rudolph³

et al. 2022

Front. Virtual Real.

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Augmented Reality (AR) as a technology in the business area is utilized in new frontiers such as collaborative real-time experiences and cloud-based solutions. However, there is still a strong tendency towards game engines, which hinders widespread adoption for businesses. We present a collaborative AR framework (Flutter plugin) aimed at lowering the entry barriers and operating expenses of AR applications. A cross-platform and cloud-based solution combined with a web-based content management system (cloud) is a powerful tool for non-technical staff to take over operational tasks such as providing 3D models or moderating community annotations. To achieve cross-platform support, the AR Flutter plugin builds upon ARCore (Android) and ARKit (iOS) and unifies the two frameworks using an abstraction layer written in Dart. In this extensive description we present an in-depth summary of the concepts to realize the framework and prove its performance being on the same level as the native AR frameworks. This includes application-level metrics like CPU and RAM consumption and tracking-level qualities such as keyframes per second used by the underlying SLAM algorithm, detected feature points, and area of tracked planes. Our contribution closes a gap in today’s technological landscape by providing an AR framework with the familiar development process of cross-platform apps. Building upon on a content management system (cloud) and AR can be a game changer to achieve business objectives, while being not restrained to stand-alone single-purpose apps. This will trigger a potential paradigm shift for previously complex-to-realize applications relying on AR, e.g., in production and planning. The AR Flutter plugin is fully open-source, the code can be found at: https://github.com/CariusLars/ar_flutter_plugin.

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Section: Related Workmentioning

confidence: 99%

Cloud-based cross-platform collaborative augmented reality in flutter

Carius¹,

Eichhorn²,

Rudolph³

et al. 2022

Front. Virtual Real.

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“…Moreover, the game itself can adapt and change its content based on the surrounding environment. The newly introduced Superhuman Sports genre contains highly interactive AR sports games, where the understanding of the surrounding allows virtual objects, e.g., a ball, to react naturally towards a collision with the environment [68]. Not only gaming, but also other AR fields can benefit from the additional knowledge that is efficiently inserted in the SLAM system with a small computational overhead.…”

Section: Proof Of Concept: Ar Applicationmentioning

confidence: 99%

EnvSLAM: Combining SLAM Systems and Neural Networks to Improve the Environment Fusion in AR Applications

Marchesi

Eichhorn

Plecher

et al. 2021

IJGI

Self Cite

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Augmented Reality (AR) has increasingly benefited from the use of Simultaneous Localization and Mapping (SLAM) systems. This technology has enabled developers to create AR markerless applications, but lack semantic understanding of their environment. The inclusion of this information would empower AR applications to better react to the surroundings more realistically. To gain semantic knowledge, in recent years, focus has shifted toward fusing SLAM systems with neural networks, giving birth to the field of Semantic SLAM. Building on existing research, this paper aimed to create a SLAM system that generates a 3D map using ORB-SLAM2 and enriches it with semantic knowledge originated from the Fast-SCNN network. The key novelty of our approach is a new method for improving the predictions of neural networks, employed to balance the loss of accuracy introduced by efficient real-time models. Exploiting sensor information provided by a smartphone, GPS coordinates are utilized to query the OpenStreetMap database. The returned information is used to understand which classes are currently absent in the environment, so that they can be removed from the network’s prediction with the goal of improving its accuracy. We achieved 87.40% Pixel Accuracy with Fast-SCNN on our custom version of COCO-Stuff and showed an improvement by involving GPS data for our self-made smartphone dataset resulting in 90.24% Pixel Accuracy. Having in mind the use on smartphones, the implementation aimed to find a trade-off between accuracy and efficiency, making the system achieve an unprecedented speed. To this end, the system was carefully designed and a strong focus on lightweight neural networks is also fundamental. This enabled the creation of an above real-time Semantic SLAM system that we called EnvSLAM (Environment SLAM). Our extensive evaluation reveals the efficiency of the system features and the operability in above real-time (48.1 frames per second with an input image resolution of 640 × 360 pixels). Moreover, the GPS integration indicates an effective improvement of the network’s prediction accuracy.

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“…Another project [33] by Eichhorn et al is exploring the possibilities of using a tracked and controllable drone in a spherical cage as a playing ball in the field of Superhuman Sports. Ubi-Interact has been successfully utilized to communicate game state and player actions here.…”

Section: Serious Games Ar Escape Room Superhuman Sportsmentioning

confidence: 99%

Ubi-Interact: A modular approach to connecting systems

Weber¹,

Ludwig²,

Klinker³

2021

ICST Transactions on Mobile Communications and Applications

Self Cite

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Ubi-Interact is a framework for interactive applications combining individual systems and devices distributed over a network. Specification and implementation of such applications should be modular, extendable and reusable. Performance, re-usability of once established capabilities and easy integration of devices are main objectives. It relies on extendable common data formats. Edge computing capabilities allow to analyze and transform this data. These computing modules can also manage an arbitrary number of devices with similar capabilities interchangeably. The framework is ready to be used. Nodes for CSharp, Javascript, C++ and Java exist and features are continuously expanded. Ubi-Interact lets users build real-time systems with modular components that can separate system behaviour from base API calls, leaving the user free to explore and maintain a combination of software and hardware each running in their native environment.

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Catching the Drone - A Tangible Augmented Reality Game in Superhuman Sports

Cited by 14 publications

References 26 publications

Cloud-based cross-platform collaborative augmented reality in flutter

Cloud-based cross-platform collaborative augmented reality in flutter

EnvSLAM: Combining SLAM Systems and Neural Networks to Improve the Environment Fusion in AR Applications

Ubi-Interact: A modular approach to connecting systems

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