Evidence map›Paper›PMID 35891058›Full record

ArticleSensors (Basel, Switzerland)2022

Benchmarking Built-In Tracking Systems for Indoor AR Applications on Popular Mobile Devices.

Emanuele Marino, Fabio Bruno, Loris Barbieri, Antonio Lagudi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Emanuele MarinoDepartment of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, P. Bucci, 87036 Rende, Italy.ORCID 0000-0002-0887-1201
Fabio BrunoDepartment of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, P. Bucci, 87036 Rende, Italy.ORCID 0000-0002-9755-475X
Loris BarbieriDepartment of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, P. Bucci, 87036 Rende, Italy.ORCID 0000-0001-7771-6582
Antonio LagudiDepartment of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, P. Bucci, 87036 Rende, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As one of the most promising technologies for next-generation mobile platforms, Augmented Reality (AR) has the potential to radically change the way users interact with real environments enriched with various digital information. To achieve this potential, it is of fundamental importance to track and maintain accurate registration between real and computer-generated objects. Thus, it is crucially important to assess tracking capabilities. In this paper, we present a benchmark evaluation of the tracking performances of some of the most popular AR handheld devices, which can be regarded as a representative set of devices for sale in the global market. In particular, eight different next-gen devices including smartphones and tablets were considered. Experiments were conducted in a laboratory by adopting an external tracking system. The experimental methodology consisted of three main stages: calibration, data acquisition, and data evaluation. The results of the experimentation showed that the selected devices, in combination with the AR SDKs, have different tracking performances depending on the covered trajectory.

Indexed as

Augmented RealityCalibrationComputers, HandheldApple ARKitbenchmarkingGoogle ARCoremobile augmented realitysimultaneous localization and mapping (SLAM)tracking accuracy

Identifiers

PMID35891058
PMCPMC9320911

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.