Research and Application of Indoor Guide Based on Mobile Augmented Reality System

Yan, Xingya; Liu, Wei; Cui, Xiaoyun

doi:10.1109/icvrv.2015.48

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…In addition, the reference lists of all retrieved papers were reviewed to determine other relevant articles. The studies in this survey are organized into the following three groups: active tracking [17]- [19], passive tracking [20]- [22], and hybrid tracking [5], [23], [24].…”

Section: Methodsmentioning

confidence: 99%

“…To verify the performance of the feature extraction module, Feature points of indoor environment images for FAST, SURF, SIFT, and FAST-SURF are tested, respectively. As the results are shown in Table 2 [23]. The oriented FAST and rotated BRIEF (ORB) detector [11] combines FAST keypoint detectors with specified binary robust independent elementary features (BRIEF) descriptions.…”

Section: Methodsmentioning

confidence: 99%

“…The hybrid indoor augmented reality tracking system integrates the virtual object into the same position in real views. The feature extractor algorithms [23], are enhanced by merging FAST with SURF to create FAST-SURF that can fit the demand for robustness and real-time performance. To define the mobile phone position information achieved by using the fingerprinting technique algorithm.…”

Section: Hybrid Trackingmentioning

confidence: 99%

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Survey of indoor tracking systems using augmented reality

Shewail

Elsayed²,

Zayed³

2023

IJ-AI

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<p><span lang="EN-US">Augmented reality overlays virtual content on the physical world, displaying location-based information more efficiently. Tracking is a trace detail of the location recorded, either by taking a reading based on a set time interval, set distance, and change in direction by more than a certain angle, or a combination of these. Tracking is divided into two types, outdoor tracking, and indoor tracking. Augmented reality added value and information to tracking applications, whether indoor or outdoor. Recently, outdoor tracking by the global positioning system (GPS) became an essential component in navigation applications. However, indoor tracking is still challenging in the augmented reality field. Most augmented reality indoor tracking use optical and sensor-based tracking, such as markers, beacons, Raspberry Pi, and route planner modules for the building. With the technology enhancement, indoor tracking started to rely on WiFi, Bluetooth, geographic information system, radiofrequency identification, and sensor chip technologies. Our paper describes the recent augmented reality tracking techniques from 2011 to 2021. The paper compares image detection algorithms with various communication technologies, disscusing the advantages and the limitations of each technology.</span></p>

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Hybrid Trackingmentioning

confidence: 99%

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Survey of indoor tracking systems using augmented reality

Shewail

Elsayed²,

Zayed³

2023

IJ-AI

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“…Yan et al [94] also used WiFi information to increase the accuracy and improve the processing time of a natural feature extraction algorithm, which combined Features from Accelerated Segment Test (FAST) with SURF.…”

Section: Characteristicsmentioning

confidence: 99%

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Morar

Moldoveanu

Mocanu

et al. 2020

Sensors

122

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Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

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