Integrating a Head-mounted Display with a Mobile Device for Real-time

Augmented Reality Purposes

Bruno Madeira

1 a

, Pedro Alves

1 b

, Anabela Marto

2,3 c

, Nuno Rodrigues

2 d

and Alexandrino Gonc¸alves

2 e

ESTG, Polytechnic of Leiria, Morro do Lena, Leiria, Portugal

CIIC, ESTG, Polytechnic of Leiria, Morro do Lena, Leiria, Portugal

INESCT TEC, Rua Dr. Roberto Frias, Porto, Portugal

Keywords:

Augmented Reality Glasses, Mobile Devices, SIMD, Display Calibration, Industry 4.0.

Abstract:

Following the current technological growth and subsequent needs felt by industries, new processes should be

adopted to make tasks simpler. Using Augmented Reality in conjunction with other technologies, it is pos-

sible to develop innovative solutions that aim to alleviate the difﬁculty of certain processes in the industry,

or to reduce the time of their execution. This article addresses one of the possible applications of new tech-

nologies in the industry, using devices that allow the use of Augmented Reality without requiring much or

no physical interaction by workers or causing many distractions, thus giving relevant information to the work

to be performed without interfering with the quality of it. It will focus, more precisely, on integrating the

Head-Mounted Display Moverio BT-35E with a mobile device and in describing the needed conﬁgurations for

preparing this device to show information to warehouse operators, using Augmented Reality, provided by a

software that runs on a capable device, discussing also what are the main mishaps discovered with the use of

this device.

1 INTRODUCTION

With the growth in the industry and its processes, it is

necessary to combine new technology developments

to make certain processes faster and more agile. One

of the new emerging technologies that can enhance

industry processes is Augmented Reality (AR). AR is

a way of viewing an enhanced version of the physical

real-world with over imposed virtual artifacts gener-

ated by a processing unit. Using it in the industry

enables tasks like order picking in a warehouse to be-

come much simpler to the operator, with the availabil-

ity of virtual information about the user’s surround-

ings and information about tasks to do, enhancing his

perception and interaction (Julie Carmigniani, 2010).

There are different options where to run and/or

present AR applications, for example, Smartphones,

Tablets, and Head-Mounted Displays (HMD), mak-

https://orcid.org/0000-0001-7991-9607

https://orcid.org/0000-0002-3869-6125

https://orcid.org/0000-0001-6005-288X

https://orcid.org/0000-0002-0953-6018

https://orcid.org/0000-0002-5966-3218

ing difﬁcult the process to choose one since it has to

be kept in mind if the chosen device supports process-

ing data or if it needs a parallel device to do the com-

puting, or even if it suits the case. In our case, the

need for a device with minimal or no interaction with

the user was the perfect solution. In this paper, is de-

scribed how an HMD device, the Moverio BT- 35E,

can be conﬁgured so that it can be used for AR pur-

poses.

The device is meant to be used in a project cur-

rently under development, named ARWare. The

project aims to develop software for companies to im-

prove their organization, management, and the efﬁ-

ciency of logistics and picking operations in ware-

houses. The software includes technologies and

methodologies such as the Internet of Things (IoT),

Industry 4.0, AR, 2D/3D Mapping, image processing,

and the use of intelligent algorithms – based on Arti-

ﬁcial Intelligence (AI) – to achieve a quick and more

optimized route for operators to pick the objects.

By using this software, it is intended that daily op-

erations in warehouses would become easier for the

operator’s and can be performed more efﬁciently, with

the combination of all logistics information that is

Madeira, B., Alves, P., Marto, A., Rodrigues, N. and Gonçalves, A.

Integrating a Head-mounted Display with a Mobile Device for Real-time Augmented Reality Purposes.

DOI: 10.5220/0010338703130319

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 1: GRAPP, pages

313-319

ISBN: 978-989-758-488-6

313

stored and available in the ERP/WMS (Enterprise Re-

source Planning/Warehouse Management Systems)

systems, the use of AR, precise indoor location and

intelligent route optimization using AI algorithms.

Combining all this information and technologies,

contextual information related to the action that the

operator is performing at that moment should be trig-

gered. Its compatibility with AR technology is tar-

geted for reducing the possibility of errors from the

operator, optimizing operations’ efﬁciency.

This paper will be important to the research com-

munity due to the lack of documentation and details

in this matter and due to the use and development of

all the innovative technologies and processes involved

in the overall solution.

2 STATE OF THE ART

Nowadays AR is becoming a widely adopted tech-

nology especially for advertisement, commercial pur-

poses, and gaming. As an example, Apple offers an

AR view of almost all of its products so that cus-

tomers can be aware of the size and how the product

looks. To be able to see the product in AR, the users

must visit the product page of the item in a smart-

phone or tablet and look for the icon to show in AR,

then, ﬁnd a surface and the object will be placed, as

illustrated in Figure 1a.

Ikea, the Swedish furniture retailer, developed an

App, called IKEA Place, where it enables users to get

a glimpse about how a product would look in their

homes with 98% accuracy of the size of the item,

since the app scales the item based on the size of

the user’s room (Ozturkcan, 2020).This way, shoppers

can be more conﬁdent about what they are buying and

if it will ﬁt their needs and taste. An example of this

can be seen in Figure 1b.

The mobile gaming industry is also adopting AR

technology to enhance their games experience to the

users. One great example of that is the widely known

game Pok

emon Go

developed by Niantic. The game

uses AR to let users catch or play with Pok

emons that

can be placed virtually in the world as illustrated in

Figure 1c.

Besides these areas, the industry is another area

that is starting to use AR technologies to help im-

prove and simplify some of their tasks and workﬂow.

One example of this is the use of AR software that

gives aid to operators on how to ﬁx, maintain or even

get help from the owner’s manual regarding machines

that they operate (Aleksy et al., 2014). Using AR,

https://pokemongolive.com/en/

Figure 1: Different AR applications. (a) Apple iPad showed

in AR. (b) Ikea Place APP

. (c) Pok

emon Go.

operators can get a more interactive view of the in-

formation regarding a task and also use this technol-

ogy to get help from another worker or assistant on-

line and without the need to schedule an appointment

on the local. Processes like these are possible by the

use of TeamViewer Pilot

, for example, an AR ap-

proach of this remote access software (Riccardo Ma-

sonia, 2017) also presented an application for industry

products maintenance purposes.

Another example of the use of AR in industries

is the order picking process in warehouses. Tradi-

tionally, the process of order picking in warehouses

has been made using paper lists, without any or much

technology support. Nowadays, most warehouses re-

sort to the use of new technologies to help speed up

and make more practical all the order picking pro-

cess in the logistical process. These technologies can

range from mobile terminals with built-in scanners so

that the operator can scan bar codes, to pick by light

or pick by voice systems (Reif et al., 2010). Taking

this into consideration, adopting the use of AR for this

purpose, as shown by (Schwerdtfeger and Klinker,

2008), where a software was developed to use with an

HMD device to help this process, can help workers to

https://www.ikea.com/au/en/customer-service/

mobile-apps/say-hej-to-ikea-place-pub1f8af050

https://www.teamviewer.com/en-us/

augmented-reality/

GRAPP 2021 - 16th International Conference on Computer Graphics Theory and Applications

314

be more productive in their tasks. Similar implemen-

tations with the same purpose of helping warehouse

order picking processes and using HMD’s where also

presented by Ubimax

in partnership with DHL

and

another solution presented by SAP

3 AR IMPLEMENTATION

3.1 ARWare Project

The ARWare project is being developed by multiple

teams and involving various technologies. These are

distributed in smaller pieces of software/applications

that will integrate with the overall solution, such as:

Route Planning software, ERP (to manage all the data

and information of the enterprise), Approximate Lo-

cation software, Indoor Fine Location Application,

and AR Application. All these components com-

municate with each other using an Enterprise Ser-

vice Bus (ESB) from Microsoft Azure with the use

of Topics (Publish/Subscribe) and also Services (Re-

quest/Response) or in the case of the communication

between the AR and Indoor Fine Location Applica-

tions, with the use of UDP Sockets. The architecture

of the overall software solution can be observed in

Figure 2.

Figure 2: General Software Architecture.

In the following subsections of this chapter, the

information will be focused on the AR component of

this software, mostly in the calibration process and

usage of the chosen AR glasses, the Moverio BT-35E

by Epson, also described latter.

3.2 Hardware

The Moverio BT-35E is a binocular Optical See-

through device that can be used for AR approaches.

https://www.ubimax.com/

https://www.youtube.com/watch?v=I8vYrAUb0BQ

https://www.youtube.com/watch?v=OrYHJaSAxis

The device, illustrated in Figure 3, has a camera

and multiple sensors. However, it does not have pro-

cessing capabilities required for AR applications and

only works as an input or output device. To run any

software, an external device that handles the process-

ing must be used, such as a computer or a mobile

phone.

Figure 3: Moverio BT-35E.

Despite the device’s hardware AR capabilities, it

was not found any AR Software Development Kits

(SDKs) that support the device before the date of this

writing. There are two types of SDK’s: the BT- 35E’s

SDK which allows accessing the camera, and sen-

sors data; and there are AR SDKs, such as Wikitude,

Maxst, and EasyAR, that can receive image data from

an arbitrary source and can, therefore, be integrated

with the device. However, none of these SDKs can

process images in the RGB565 format, which is used

by the BT-35E SDK, and none allow, at least for free,

to calibrate the device’s displays which are needed to

use spatially aware augmentation.

This document presents an integration of the BT-

35E with the Huawei P20 mobile device, which en-

tails the implementation of display calibration using

Open Computer Vision library (OpenCV) aruco mod-

ule and image conversion via hardware-speciﬁc in-

structions. The mentioned functionalities were inte-

grated with the Unity game engine.

3.2.1 Rendering

BT-35E display technology can be classiﬁed as con-

ventional stereoscopic 3D, using the taxonomy in

(Zabels et al., 2019, p. 6).

The device allows projecting a 2D video source

at a ﬁxed, unspeciﬁed

, vergence distance. Alterna-

tively, it has a 3D mode that splits the source in half

and displays each half entirely in each of the displays.

Both modes can be observed in Figure 4.

This means that the rendering content for the BT-

35E’s displays can be done in the same way that it is

done for any 2D screen.

Rendering stereoscopic content can be achieved

by drawing to each half of the display using virtual

Supposing it is the same as the default on previous

models, the vergence distance is around 11 meters.

Integrating a Head-mounted Display with a Mobile Device for Real-time Augmented Reality Purposes

315

Figure 4: BT-35E display modes. (a) 2D mode. (b) 3D

mode.

cameras that model the projection of the correspond-

ing eye.

3.3 Image Format Conversion

The RGB565 is a pixel color format that describes an

image in red, green, and blue color components. The

format is very similar to the RGB24 format, which

contains 8 bits per pixel color channel, but RGB565

discards the 3 least signiﬁcant bits of the red and blue

components, and 2 bits of the green component.

Since this format is not supported by OpenCV, the

source image used by the BT35-E SDK needs to be

converted. Additionally, the conversion process needs

to be efﬁcient to achieve a stable frame rate, minimize

the latency of AR-related updates, and avoid thermal

throttling.

In (Wagner and Schmalstieg, 2007), this conver-

sion is done using lookup tables into the LUM8 for-

mat; an advantage of this approach is that it is agnostic

to hardware.

Unlike the above-presented method, our imple-

mentation relies on hardware capabilities. It borrows

ideas from the libYUV library

, and implements im-

age conversion in assembly for the NEON64 SIMD.

GPU based conversion was also implemented prior to

the SIMD solution.

An image format conversion library optimized for

SIMD architectures (Google Inc., 2020).

3.3.1 GPU

GPU based conversion was seen as the easiest solu-

tion to integrate due to the following reasons:

• Unity, the target development platform, has built-

in tools to implement, compile, and use compute

shaders. Dissimilarly, a SIMD-based solution re-

quires the implementation of a plugin;

• The conversion to the RGBA32 format using

High-Level Shader Language (HLSL) is trivial to

implement;

• An HLSL implementation is agnostic to graph-

ics API; and is, therefore, more portable than

architecture-speciﬁc assembly implementations.

It was ﬁrst implemented image conversion for the

color format RGBA32, which resulted in near 30

conversions per second with an image resolution of

1280x720 pixels when tested in isolation, i.e., with-

out using a AR module.

The above results show that GPU-based image

conversion may be enough for some applications;

however, they are not conclusive regarding real-time

AR applications. To determine whether it was a vi-

able solution when using real-time AR, the same test

was done using Maxst SDK 4.x marker detection over

the converted image. Note that camera or display cal-

ibration was not required to test performance. When

using the Maxst SDK the performance fell to around

12 to 16 conversions per second, and the device would

heat up considerably.

It was noticed that changing graphics API affected

performance; the default API, OpenGLES3 API, had

better performance than Vulkan.

We also implemented image conversion to a single

channel grey image, and RGB24

color formats. Both

formats can be used with OpenCV; however, none im-

proved the performance of the image conversion pro-

cess signiﬁcantly.

The OpenCV aruco module had worse perfor-

mance than the Maxst SDK; which meant that our

GPU-based image conversion implementation was

not suited for manual display calibration, and there-

fore, not suited for real-time AR either.

3.3.2 SIMD

To try to achieve better results it was implemented

image conversion to the color formats RGBA32 and

RGB24. This approach outperformed the GPU so-

The RGB24 format, due to the limitations of the data

types available in compute shaders, does not allow a stream-

lined conversion; it requires the addition of an unused mar-

gin.

GRAPP 2021 - 16th International Conference on Computer Graphics Theory and Applications

316

lutions, allowing a maximum conversion rate of near

60 frames per second (fps) using the same resolution;

however, prolonged use would cause it to heat up and

drop performance.

We obtained a conversion rate of approximately

30 and 19 fps using the Maxst SDK and OpenCV re-

spectively, at a resolution of 1280x720 pixels.

Although results were below 30 fps, below the dis-

play refresh rate, they were good enough for calibra-

tion purposes, although not ideal. When calibrating

the device, to ensure a stable frame rate, and avoid

heating the device, we locked the rate of image out-

puts by the BT-35E SDK at a frequency of 15 fps.

3.4 Display Calibration

Via a display calibration method, the eyes’ projection

matrices are estimated. This calibration can be clas-

siﬁed, according to (Grubert et al., 2018), as follows:

manual, requiring user interaction; automatic, done

without user intervention; or semi-automatic by re-

ducing user inputs thought some automatic process

typically done apriori. The methods we implemented

fall under the family of manual calibration methods.

3.4.1 SPAAM

The ﬁrst implemented display calibration method was

the Single Point Active Alignment Method (SPAAM)

(Tuceryan et al., 2002). Since SPAAM requires no

additional hardware since it is a manual method, and

because it is well documented, being that the origi-

nal paper shows the required steps to integrate it with

Open Graphics Library (openGL), it was a prime can-

didate as a ﬁrst display calibration method implemen-

tation.

Our implementation uses a board with 6 ArUco

markers. The center of the board is marked with a

cross-hair image that indicates the world point that

the user needs to align with the on-screen displayed

points, as illustrated in Figure 5b. To present 3D

content that respects real-world coordinates, not nec-

essarily displayed over ArUco markers only, camera

calibration was done before the display calibration us-

ing a ChArUco board.

With the results presented in (Axholt et al., 2011)

as a reference point, the number of points per display

to align in our implementation was set to 25. No par-

ticular point distribution was used, but it was taken

into account that there should be variation in points

depth.

Regarding the efﬁciency of the calibration pro-

cess, (Wagner and Schmalstieg, 2007) compares the

time that different, but similar, calibration methods

take, and SPAAM shows the worst results. With this

Figure 5: SPAAM calibration procedure. (a) Board where

to align the cross-hair during calibration; (b) Example of

what is seen through one of the device’s display during the

calibration process.

in mind and having experienced the cumbersome pro-

cess of the SPAAM calibration, an additional calibra-

tion method was conceptualized and implemented.

3.4.2 FABSAM

Fixed Axis Bi-dimensional Shape Alignment Method

(FABSAM), is the novel term here presented, to de-

note a manual calibration method in which the user

aligns a shape along a ﬁxed axis, at different depths.

FABSAM is a hybrid between the depth-SPAAM

and MPAAM calibration methods described in (Tang

et al., 2003) and (Grubert et al., 2020) respectively.

Similar to MPAAM, the user has to align a group of

points, but these are not distributed at different depths.

Then, similarly to depth-SPAAM, the user needs to

repeat the previous procedure at different depths. In-

stead of being made aware of the points to align, the

user is presented with a shape that is displayed at dif-

ferent sizes, which in our implementation is a board

composed of aruco markers, depicted in Figure 6b.

The points to be matched are within the shape and are

set via the Unity editor.

Figure 6: FABSAM calibration procedure. (a) User align-

ing the displayed board with the board on the wall. (b) Im-

age of the calibration board shown on the left display of

the AR glasses, horizontally shrink-ed due to display stream

setup.

Integrating a Head-mounted Display with a Mobile Device for Real-time Augmented Reality Purposes

317

The conceptualization of this method was rooted

in the following assumptions:

1. Calibration point depth distribution is more im-

portant than alignment noise.

The method enforces depth distribution by pre-

senting an image at different sizes, which forces

the user to change their distance to the target.

Note that BT-35E’s camera direction is approxi-

mately aligned with the user’s orientation when

facing the board so that the camera distance to the

board is like the user distance to the board. This

assumption is rooted in the ﬁndings presented in

(Axholt et al., 2011) and (Moser et al., 2014).

2. Display curvature is negligible. In the case of the

BT-35E, this is applicable because the displays are

plane by their small size and ﬁeld of view.

3. Eye orientation changes are negligible.

Because the user aligns a shape instead of a sin-

gle point, we suppose the eye orientation remains

ﬁxed as depicted in Figure 7; however, we do not

have any data to back up this assumption.

The calibration procedure is assistance free: it

does not use a device to mitigate user shakiness

or enforce correct alignment at different depths;

thus, it may not be negligible regarding eye orien-

tation, unlike supposed.

Figure 7: Fixed Axis Bi-dimensional Shape Alignment

Method.

Note that both SPAAM and FABSAM only need

to know the position of the target points to align to the

camera coordinate system, and, despite what our im-

plementations might suggest, there is no need to have

a target point superimposed over an aruco marker or

board. The main utility of being superimposed is that

it is portable, however, this is not a requirement. The

markers are not a requirement either if the target po-

sition can be known through a different method. Al-

though not tested, it is plausible that having an en-

vironment covered in markers, not necessarily aruco

or id-based markers, would allow estimating the tar-

get points position with higher accuracy, thus result-

ing in a better calibration. Such consideration led to

the conceptualization of an alternate FABSAM cali-

bration protocol in which the target height is adjusted

to the user’s eyes height and used as input to grasp its

position to non-superimposed markers on the calibra-

tion environment.

4 DISCUSSION

In sections 3.3.2 and 3.4.2, we presented a minimum

viable solution for AR SDKs integration and display

calibration. The presented solution performance is,

however, not ideal, running at a lower frequency than

the minimum display refresh rate supported by the

BT-35E displays, 30 fps.

A possible improvement regarding the perfor-

mance limitations of our solution is to improve data

workﬂow by, for example: converting the image di-

rectly into a single channel instead of 3 RGB channels

which are converted by OpenCV aruco module into

one subsequently; replace OpenCV operations with

ARM Compute Library (ACL) such as threshold; im-

proving data locality if possible. An alternate solution

might be to use the Neural Processing Unit (NPU) of

the mobile device to detect the aruco markers. (Hu

et al., 2018) has implemented a neural network-based

solution that outperforms traditional marker detection

for ChArUco boards. Although, we are not knowl-

edgeable enough to comment on whether this ap-

proach is viable or not for the target hardware.

The calibration methods yielded acceptable qual-

itative results, but no data was collected or analyzed

to make a rigorous evaluation. In our calibration at-

tempts, FABSAM topically took less time to cali-

brate. Additionally, was noticed that SPAAM would

sometimes result in bad calibration due to bad aligned

points, which did not happen when calibrating with

FABSAM.

Regarding the details of the FABSAM, in our im-

plementation, the points to match are contained in-

side the shape to align. An alternate implementation

could consider points outside of the shape to align, so

that point distribution is not higher towards the center.

This could impact the calibration quality.

5 CONCLUSION AND FUTURE

WORK

This paper describes the utilization of AR technol-

ogy with a focus on industry applications, more pre-

cisely the use in warehouses. It is presented which

GRAPP 2021 - 16th International Conference on Computer Graphics Theory and Applications

318

factors should be taken into consideration when using

the Moverio BT-35E binocular Optical See-Through

device. Although somewhat capable, this device is

not the best suited for AR purposes, due to the lack

of speciﬁc System Development Kits for their setup

and usage. That said, this paper focus on techniques

and procedures on how to get this device setup for AR

usage, trying to circumvent the mishaps that currently

exist.

Some obstacles were raised during the develop-

ment, like the lack of System Development Kits that

provide device display calibration for free and/or

weren’t fully compatible with each other. This cre-

ated the need for developing a solution for that, where

performance was also an obstacle to take into account

and discussed in the paper.

In the future it is expected that the concepts and

processes discussed here in this article will be applied

in the project under development, more precisely us-

ing the device with an AR software in support for

warehouse order picking processes. This way, it is

possible to help a warehouse operator to perform his

tasks in a simpler and faster way, without great com-

plexity or previous knowledge required. This aid may

be provided using the information and visual instruc-

tions on the Epson BT-35E device, thus informing the

user of the current status of a task, which must be

taken inside a warehouse when executing a route con-

sisting in the picking of products, among others.

ACKNOWLEDGMENTS

This work is co-ﬁnanced by the ERDF - European Re-

gional Development Fund under the Operational Pro-

gram for Competitiveness and Internationalization -

COMPETE 2020 of Portugal 2020 through ANI - Na-

tional Innovation Agency - Project ”AR WARE: Aug-

mented Reality for intelligent WAREhouse manage-

ment: POCI- 01-0247-FEDER-033432”.

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