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Pedestrian Augmented Reality Navigator

Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphone...

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Autores principales: Mahapatra, Tanmaya, Tsiamitros, Nikolaos, Rohr, Anton Moritz, K, Kailashnath, Pipelidis, Georgios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965418/
https://www.ncbi.nlm.nih.gov/pubmed/36850414
http://dx.doi.org/10.3390/s23041816
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author Mahapatra, Tanmaya
Tsiamitros, Nikolaos
Rohr, Anton Moritz
K, Kailashnath
Pipelidis, Georgios
author_facet Mahapatra, Tanmaya
Tsiamitros, Nikolaos
Rohr, Anton Moritz
K, Kailashnath
Pipelidis, Georgios
author_sort Mahapatra, Tanmaya
collection PubMed
description Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple’s framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone’s motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app’s accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data.
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spelling pubmed-99654182023-02-26 Pedestrian Augmented Reality Navigator Mahapatra, Tanmaya Tsiamitros, Nikolaos Rohr, Anton Moritz K, Kailashnath Pipelidis, Georgios Sensors (Basel) Article Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple’s framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone’s motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app’s accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data. MDPI 2023-02-06 /pmc/articles/PMC9965418/ /pubmed/36850414 http://dx.doi.org/10.3390/s23041816 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mahapatra, Tanmaya
Tsiamitros, Nikolaos
Rohr, Anton Moritz
K, Kailashnath
Pipelidis, Georgios
Pedestrian Augmented Reality Navigator
title Pedestrian Augmented Reality Navigator
title_full Pedestrian Augmented Reality Navigator
title_fullStr Pedestrian Augmented Reality Navigator
title_full_unstemmed Pedestrian Augmented Reality Navigator
title_short Pedestrian Augmented Reality Navigator
title_sort pedestrian augmented reality navigator
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965418/
https://www.ncbi.nlm.nih.gov/pubmed/36850414
http://dx.doi.org/10.3390/s23041816
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AT kkailashnath pedestrianaugmentedrealitynavigator
AT pipelidisgeorgios pedestrianaugmentedrealitynavigator