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Error-Robust Indoor Augmented Reality Navigation:
Evaluation Criteria and a New Approach
Oliver Scheibert
TH Köln
Steinmüllerallee 151643, Gummersbach, Germany
scheibert.oliver@gmail.com

Jannis MöllerSteve Grogorick Martin Eisemann
TU Braunschweig
Mühlenpfordtstr. 23
38106, Braunschweig, Germany{moeller,grogorick,eisemann}@cg.cs.tu-bs.de

ABSTRACT
Tracking errors severely impact the effectiveness of augmented reality display techniques for indoor navigation.
In this work we take a look at the sources of error and accuracy of existing tracking technologies. We derive
important design criteria for robust display techniques and present objective criteria that can be used to evaluate
indoor navigation techniques without or in preparation of quantitative user studies. Based on these criteria we
propose a new error tolerant display technique called Bending Words, where words move along the navigation path
guiding the user. Bending Words outranks the other evaluated display techniques in many of the tested criteria andprovides a robust, error-tolerant alternative to established augmented reality indoor navigation display techniques.

Keywords
Augmented Reality; Robust Indoor Navigation; Display Techniques; Perceived accuracy; Tracking errors;

1

INTRODUCTION

Augmented Reality (AR) device tracking systems are
not perfect and errors can accumulate over time enforcing cognitive compensation of the user [MMC00]. In
this work we compare the robustness of AR display
techniques (interfaces) for indoor navigation to provide
useful navigation information even in the presence of
tracking errors.
AR provides location-aware user experience by overlaying spatially registered, digital information on a screen
for real-time interaction with the physical and virtual environments [BCL15]. An important application scenario
is pedestrian navigation. With recent advances in user
tracking technologies and sufficient processing powerof modern smartphones, the more challenging indoor
navigation has become feasible [MKH+ 12].
However, the technology is still in its infancy and reliability for an extended amount of time has not beenachieved [YNA+ 17,FPS+ 20]. McIntyre et al. stated thatthe problem of accumulation errors within a tracking
system will not be solved in the near future [MCJ02].
And 20 years later he is still right. Relying on such afaulty system results in digital objects being far off their

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supposed position. One way to tackle this problem is
to increase the user’s awareness of the tracking imperfections using different display techniques [PDCK13],
e.g., by using 3D arrows that can change in color andshape, or similar feedback. Error visualization generally
improves AR navigation systems but it is challenging todesign suitable visualizations [PDCK13]. Even worse
the system might not be aware of the tracking errors,
lulling the user into false security.
In order to reduce the impact of tracking errors on indoor
navigation instead of only making users aware of it, we
contribute the following: We first define the problem
of uncertain tracking errors in the context of tracking
systems (section 2). Next, we classify AR navigation
display techniques (section 3), and investigate typicalrepresentatives. We present objective criteria to evaluate
AR display techniques with regard to error-robustness(section 4) and evaluate the representatives (section 5).
In section 6, we propose our own display technique
Bending Words and discuss (section 7) its advantages.

2

RELATED WORK

Pedestrian navigation services have gained attention forseveral years now [MRBT03,RC17]. They evolved from
2D paper maps to digital maps on mobile devices tolocation-based turn-by-turn instructions [Kim10]. Modern positioning systems enable AR systems to guidethe user in real-time. These systems require a very
high accuracy to correctly display information and avoid
confusion and misguidance of the user. Yet, there is
still no generally accepted solution for localization systems [MKH+ 12], e.g., Adler et al. who analyzed

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assumed
real
goal
goal

and categorized 183 indoor localization techniques published between 2010 and 2014 [ASWK15].
A user generally expects accuracy to be perfect or atleast good enough to rely on the displayed information
which is often a mistake and may lead to navigation failures. Visualization techniques that adapt to the amountof tracking errors have the power to decrease the impact
of inaccurate tracking solutions [PDCK13]. To understand why tracking solutions are always imperfect it isimportant to take a look at the underlying types andsources of error.
Localization
Localization is the process of tracking a user’s positionor more precisely the position of the device used fornavigation. Applications for localization include pedestrian navigation, robotics, dynamic personalized pricing,product placement, advertisement, fleet management or
intelligent spaces [YNA+ 17, LLY+ 15].
Localization systems rely on physical properties of signals, e.g. speed of light, or other measurable forces, suchas earth’s magnetic field. Sensor and information fusing may improve the overall performance, e.g. Wi-Fi
and magnetic signals [SBS+ 15] or incorporating a priori knowledge, such as a map of the environment, tomake the localization systems more robust and accurate [HFH04].
Despite constant improvements in localization techniques, a perfect solution seems almost impossible. Development times for indoor navigation systems are often several years and currently might not result in a
widely accepted solution which has the high precision
required to accurately display AR content within thecamera feed [LLY+ 15, MSTSP+ 21, GFW21].
The cognitive load posed on the user of indoor navigation systems correlates with the accuracy of localization.
This effect is especially prevalent in AR applications
where the wrong positioning of visual media within thecamera feed becomes distracting at best or misleading
at worst [MCJ02].
Generally, two types of error exist: Rotational error, theangular difference between the direction to the assumed
position of the next waypoint and the direction to the actual next waypoint; and translational error, the distance
between the current position and the assumed positionof the tracking device (Figure 1).

Different tracking systems have different sources anddegrees of errors. Signal-based localization techniques(Wi-Fi, GPS, Bluetooth, etc.) are more prone to errors caused by changes in the environment. Clutteredhttps://www.doi.org/10.24132/CSRN.3301.17

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assumed
goal

real
goal

d
α

d
real
positionrotational error α

assumedpositionrealpositiontranslational error dFigure 1: Types of error: Rotational errors (left) result in misleading direction information as the tracking
system’s assumed goal and the real goal differ. Translational errors (right) result in irritating positioning of
the augmented content due to disparity between the assumed position and the real position as well as assumed
goal and real goal of the tracking device.
Tracking System
Ultra-wideband Positioning [YNA+ 17]
Wi-Fi Based Positioning [YS15]
Magnetic Positioning [SBS+ 15]
Global Positioning System [LGD+ 15]
Bluetooth [RLJ+ 15]
Vision-Based Positioning [KJ08]
Pedestrian Dead Reckoning [KH15]

Accuracy
1 cm - 0.3 m
1 - 10 m
1-8m
1 - 10 m
0.5 - 10 m
resolution dependent
distance dependent

Table 1: Accuracy of user tracking systems
environments introduce multipath, non-line-of-sight,
and shadowing artifacts that affect either the arrivaltime, angle, or strength of a signal reaching the sensor [RC17, YNA+ 17]. Even the human body affects
tracking accuracy [APM+ 16].
In Table 1 we provide an overview of several tracking
techniques commonly found in pedestrian navigation
systems and list typical accuracy ranges.
AR applications require a high precision tracking in order to display the virtual content correctly. This makes
vision-based positioning systems currently the only alternative. These systems mostly rely on feature point
tracking with one or more cameras and/or depth information [KJ08]. Due to their computational demands
vision-based techniques are often coupled with fastertechniques [BEP15]. While lighting conditions, surface
properties (reflecting/refracting), and occlusion may negatively impact tracking precision [DRMS07], accuracyof vision-based positioning systems is theoretically only
limited by image resolution.
Other exotic positioning systems utilize sound, lightbeacons, FM radio, or RADAR, to localize a user within
a building but are rarely used in practice [YNA+ 17].

3
Tracking Systems

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AR NAVIGATION DISPLAYTECHNIQUES

Commonly all AR indoor navigation display techniques
require the following input:
1. The path from the current position towards the goal,
usually provided as a list of 3D coordinates

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2. The geometry of the building, including walls, doors,stairs, or elevators.
3. The user’s pose that represents their position androtation within the building.

Discrete InformationLines on the GroundWaypoint Marker
Bending Words

Guiding Information

Guiding Arrow
Shining Light
Digital Avatar
Haptic Feedback

Context Information

Paper Map
World in Miniature

Using this information the user gets feedback where tohead next in order to reach their goal.
Besides the technical accuracy of these tracking systems, the way navigation information is presented has
a strong impact on how accuracy of the navigation system is perceived by the user [PDCK13]. Misaligned
or constantly jittering visual elements are distracting atbest or misleading at worst [MCJ02]. Adapting how
these instructions are delivered strongly increases performance of users when navigating through unknown
environments [RC17]. But instead of interface design,
the focus in indoor navigation applications is mostly puton their localization techniques.

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Table 2: Display technique examples. We evaluate
the representatives most resilient against tracking errors (marked in bold) in section 5, and present Bending
Words in (section 6).

The user’s expectation of the system influences how
they perceive it. If a technique is usually perceived asvery accurate, based on past experiences, an inaccurate
tracking will be overly distracting [MBMH01]. Whereas
a less common technique which makes an imperfect
tracking state less obvious could more easily prepare theuser for the actual experience when using the system[MMC00].

Guiding Information
Guiding Information display techniques provide a series of guiding step-by-step information that are always
limited to the next waypoint. This reduces cognitive
load of the user and thereby positively affects their performance [WLPO94]. One contributing factor is theegocentric point of view that these display techniques
provide [SCP95].
An example of such a technique is a Guiding Arrow thatis positioned at the user and points towards the next waypoint [LMM16], or a Shining Light cone [MEN15]. Thishas the advantage of being perceived as less accurate
than an arrow, which can dictate the users’ expectations
beforehand. The last approach in this category is the
Digital Avatar that guides the user towards their goal bywalking ahead of them [PDCK13].

Extending on Pankratz et al. [PDCK13] we distinguishthree categories of AR navigation display techniques:
1. Discrete information which shows navigation hints
as one or a series of next steps;
2. Guiding information which shows only the direction
towards the next waypoint;
3. Context information which shows also the areaaround the user in an exocentric view.
Within these we found several representative techniques,
see Table 2. Examples for the display techniques emphasized within the table are shown in Figure 2.

A somewhat different approach to Guiding Information
display techniques is the design of interfaces for visually impaired people [ZYZH19]. In this approach
information is mapped to auditory or haptic cues (Haptic Feedback) to guide the user towards the next waypoint using different rates of tone frequencies or vibrations [ZYZH19].
Context Information

Discrete Information
Discrete Information display techniques provide information about the next steps along the path at any time.
A common example is Lines on the Ground that lead
towards the goal. Instead of providing a continuous path,waypoint markers display only the next corner. These
techniques are prone to tracking errors as they do notprovide much context information to allow the user tocompensate for the error. A typical example are nonaligned waypoints due to rotational error. A line on the
ground makes it sometimes hard to guess where the systems wants the user to go if the line is off it’s intended
direction (Figure 2, top left).

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Context Information display techniques provide the user
with additional information about their surroundings.
This includes the traditional paper map. Users are shown
the context in which they are positioned instead of direct
navigation hints. By providing landmarks or other identifiable information about their surroundings, users areable to orient themselves using these techniques even
without displaying their approximate location [BP13].
The goal of providing contextual information is to understand the connections between places within an environment and eventually help the user form their own routememory. This comes at the cost of an increased cognitive load while using the navigation system [RC17].

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0◦
5◦
10◦
15◦
0◦
5◦
10◦
15◦
Figure 2: Simulated rotational error, increased in 5◦ steps from left to right for each display technique. Top left:
Lines on the ground, top right: Bending Words (section 6), bottom left: Digital Avatar, bottom right: World in
Miniature. As a non-visual technique, Haptic Feedback is omitted here.
A classic example is World in Miniature (WIM). It essentially is a bird-eye view onto a small model of thebuilding. Self-localizing is made easier by aligning theorientation of the miniature with the orientation of thereal building and showing a marker for the position of
the user within that model [HFH04, SCP95].

4OBJECTIVE EVALUATIONCRITERIA

In the following we describe how to evaluate navigation techniques based on objective criteria. We gathered
these from multiple previous works to provide a comprehensive approach to rank display techniques within
the context of creating error-robust indoor augmented
reality navigation. They are not intended to replace systematic user studies but may be useful for preliminary
investigations beforehand or to complement small-sizeduser studies in times where elaborate user studies are
difficult to conduct, e.g. during a pandemic. The criteria are not metrics but instead provide guidelines tocompare two or more display techniques against eachother argumentatively. A score-based comparison wasconsidered, as it can show variations between methods
better, but due to the lack of calculable measures insome criteria it was decided against. Assigning eachcriterion a weight can be done and should be based on
the individual requirements and target group.
We partition the aggregated list of criteria into two maincategories: visibility (how to present information, subsection 4.1) and interaction (how to promote interaction,subsection 4.2). Each criterion within these categories
was chosen based on the relevance to not only AR interfaces in general, but also to diminish the problem
of uncertainty in tracking. Both technical aspects andhuman factors, including familiarity [AZLK12], are covered.

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4.1

Visibility

Visibility describes elements that influence the ability ofa user to perceive the navigation information and process
it. This includes the visibility of elements, how often
and how much of the information is located within thescreen, and how effective it is in guiding the user.
Deviation Range: The deviation range refers to therange in which a display technique is still perceived asconveying the right information even though tracking
errors exist [MKD+ 14]. The more precise the alignment
between the desired position within the world and theposition as shown on the display, the lower the chanceof user failures such as taking a wrong turn or following a wrong path [MSS11]. It was shown that target
detection performance decreases notably from precise(< 7.5◦ rotational error) to partially degraded (< 22.5◦ )to poor (< 45◦ ) given the increase in error [YWMB01].
Möller et al. also showed that users "perceived [wrongly
estimated orientation] more negatively than a wrongly
estimated location" [MKH+ 12]. Thus, tolerance against
angular tracking error is an important evaluation factor,
which we call the Deviation Range criteria. The display
techniques can be evaluated and compared using theabove mentioned thresholds in a qualitative manner orby comparing the overlap between the displayed content
and the next goal on the display.
Path Information Visibility: To help prevent navigation errors, instructions have to be comprehensively distributed across the path and must be visible at appropriate points in time [RC17, SK00]. An overview of
the upcoming tasks and especially the ability to easily
see the next target location can also increase the performance of users [MSS11]. To summarize these effects,
we devised this criterion where one needs to compare

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the amount of displayed path information for each technique.

4.2

one creates a more natural interaction with the systemthat grants more flexibility in a mobile situation suchas during navigation [Gri09]. It can also increase theapplication’s accessibility by allowing a user to freely
choose their preferred modality [KSS20]. And it can
reduce navigation errors of users, especially in reduced
visibility conditions or if one modality conveys ambiguous information, therefore increasing effectiveness of
the user-computer system [CFBM13].

Interaction

The way a display technique promotes interaction withthe user, influences the user experience, but also impacts
the tracking system itself. In the following we will
explain the different criteria related to user interaction
and their relevancy.

Familiarity: A familiar display technique can help users
build trust in the navigation information [AZLK12]. Existing navigation applications have introduced a set ofdisplay techniques that are widely accepted and understood, such as arrows or lines. Using these known forms
can help users understand the system’s intention whenbeing guided. New, unfamiliar, technologies can sometimes lead to inadequate user experience for users withno previous knowledge of it and increase the cognitive
load [ASB18]. This criterion is not directly measurable
and depends on previous user experiences, though we
expect differences to be mostly cultural. Therefore, aranking using the familiarity criterion must be based onargumentative comparisons.

Device Orientation: The device orientation is mainly
relevant for non-head-mounted vision-based tracking
devices such as smartphones. The natural way for auser to hold these devices is at a 45◦ angle towards theground [MKD+ 14]. This limits the number of featurepoints visible to the camera which negatively impacts theperformance of a vision-based tracking system. A display technique should therefore enforce an upright position of the tracking device in a subtle and non-disturbing
manner, which increases robustness.
Instant Feedback: Reaction time to changes in the
tracking information of a display technique is an important factor for overall performance and user experience [RC17, SG04]. A technique, which updates thedisplayed information e.g. only at the next waypoint or
at too large of a translational or rotational error can leadto deteriorated performance as the user might overshootthe target. Displaying lengthy animations are problematic for the same reasons.
Environmental Awareness: The awareness of the surrounding environment is an important factor when using
digital navigation aids in general [BP13,MOP+ 09]. It decreases the requirement to use a navigation aid over timeas the user becomes familiar with their surrounding andit also improves safety to avoid dangerous situations andobstacles. During times where the positioning system accumulates too much error to display reliable information,
users can continue their navigation in the right direction
using their acquired spatial knowledge [KAZ04]. A display technique can increase environmental awareness byusing landmarks as part of the localization method orby making the environment stand out more. Techniquesthat cover too much of the screen or require the user
to constantly look at it decrease environmental awareness. Environmental awareness using different display
techniques is not measurable but needs to be comparedargumentatively.

5

CRITERIA APPLICATION

To apply the presented evaluation criteria, we chose atleast one representative from each navigation visualization category. From the Discrete Information category
we chose lines on the ground as they generally provide more information than waypoint markers. From
the Guiding Information category we chose the Haptic
Feedback [ZYZH19], as the only non-visual navigation technique; and the Digital Avatar [PDCK13], asGuiding Arrow and Shining Light are conceptually only
specialized instantiations of the Digital Avatar. As Paper
Map is a non-digital navigation technique we opted forWorld-in-Miniature [SCP95] from the Context Information category.

Multimodality Count: Multimodality Count is a measure counting the number of natural sensory receptors
being utilized to convey information [GLB05]. In otherwords, how many senses does the display technique address? The main modalities relevant to current AR applications and the most researched within that context are:visual, auditory, and haptic modalities [Liv05, KSS20].
It is important to choose the right combination and number of modalities for the task at hand: Using more thanhttps://www.doi.org/10.24132/CSRN.3301.17

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In the following, we briefly describe the implementations of the techniques. Lines on the ground are implemented as stripes of arrows, that lead from the useralong the path (Figure 2, top left). The walking direction
is indicated by the arrow directions. The entire pathis visible as occlusions from the environment are nottaken into account. World in Miniature (WIM) shows
the surrounding area as a small model within the view
(Figure 2, bottom right). The model, including the user’s
tracked position depicted as a red dot, is fixed in frontof the user, while its orientation is constantly alignedwith the tracked orientation of the real building for an
improved user performance [WLPO94]. Besides modeland user position, the only additional information is thevery next step of the navigation path, displayed as ayellow dot. The Digital Avatar (Figure 2, bottom left),is a humanoid robot entity that guides the user towards

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the next waypoint along the path. It walks in front ofthem and waits when the user is not moving. Haptic
Feedback links angular difference between current viewing direction and next waypoint to vibration frequencyof the tracking device. If the tracking device is pointed
towards the correct direction (the next waypoint is inside the green region), it vibrates at the highest rate. If
the next waypoint is in the outer thirds of the screen it
vibrates with a medium frequency. If it is to the left or
right of the screen the slowest vibration is applied tomake the user aware that the system is still running.

5.1

Results

We obtain the deviation range ranking by measuring
the range of rotational error that a display technique
can be exposed to until it no longer overlaps with theposition of the next waypoint. We verified the results
qualitatively by simulating the rotational error in stepsof 2.5◦ (Figure 2 showing every 2nd step).
Lines on the ground shows perceptible mismatch alreadyat a small rotational error, the Digital Avatar and Haptic Feedback display accurate navigation information
within a 5◦ rotational error. The value for Haptic Feedback has been obtained by measuring the range in which
the vibration rates change. Besides the quantitative valueof the deviation range we also include unique characteristics of the display techniques. E.g., even though thedeviation range of WIM is only 5◦ it can still be used
to identify the next steps by comparing the landmarks
within the model with landmarks of the surroundings.
Concerning the path information visibility Haptic
Feedback provides the fewest information as it only
roughly points towards the next waypoint. Digital Avatar
gives hints on where the waypoint is as a user approaches
it. Because of the lack of occlusion, Lines on the groundcan show more than just the path to the next waypoint,
although information further down the path becomes
more and more disassociated to the environment. WIM
performs best, as it displays the complete environment
as a map and can potentially show the complete path.
Most techniques that rely on displaying spatially registered information, such as lines on the ground andDigital Avatar, enforce an upright device orientation of90◦ which is beneficial for feature tracking. With Lineson the ground the user has a slight tendency to look down
and follow the arrows. Haptic Feedback is designed toaid visually impaired people who don’t use visual cuesand therefore tend to hold the device at a more natural angle of 45◦ . The camera then tracks mostly thefloor which has fewer features. Linkage of the displayedmodel in WIM with the orientation of the tracking device [WLPO94] makes a rotation of smaller degrees
more likely to improve visibility of path information,
which has an inverse effect on the device orientation
criterion. WIM also never gives incentives to point thecamera in the direction of travel, making it rank worst.

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Instant feedback All techniques except Digital Avatar
update the navigation information or a deviation fromthe planned path in real-time. The Digital Avatar isrestricted to human speed to not break the immersion.
Note that this is strongly implementation dependant andshould not be generalized.
Environmental awareness describes the trade-off between providing information from the navigation technique and environmental information to avoid obstacles
and to become familiar with the environment over time.
The WIM model shows the best support by providing
context information in form of a model of the surroundings. Lines on the ground provides some but lesser
context information in the form of directional changes atjunctions and corners. Haptic Feedback was ranked third
due to its minimum amount of information. We consider
the digital avatar to be the least awareness-friendly technique, as it not only provides very little path informationand occludes a large area of the screen, but also inherently enforces the user to focus on the avatar instead of
the actual path.
The multimodality count refers to the number of naturalsenses addressed. All techniques solely rely on visual
information except haptic feedback, which vibrates thedevice and even generates acoustic feedback thereby.
Again, this is strongly implementation dependent.
The familiarity of each technique is based on how often
elements of it are found within other everyday navigation situations. Digital Avatar implements the common
situation where a user has to follow someone through an
unknown environment. Lines on the ground in a similar
form is broadly used for car and pedestrian navigation
systems. Transferring this to AR preserves this familiarity. WIM resembles the well-known paper maps. Haptic
Feedback is the least common approach as it makes
the assumption that users can correlate an increase invibration speed to positive directional feedback.

6

BENDING WORDS

Bending Words is our proposed discrete information display technique based on optimizing the criteria fromsection 4. In its core, this technique takes advantage
of a person’s ability to naturally follow turn-by-turn instructions. [Kim10] Yet, it overcomes the problem thatturn-by-turn instructions are usually unable to show theexact location of the next turn [PB10]. During navigation Bending Words shows a three-dimensional text
containing turn-by-turn instructions and adapts to thepath in front of the user (Figure 2, top right, and Figure 3) both in terms of position and displayed text. The
displayed text consists of two parts: The action keyword
which is either Go or Destination reached to indicate
if the goal is reached; and the direction keyword which
is one of right/left/straight/up/down to indicate the next
action at the next waypoint.

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The advantage of this technique is that it shows users
where to go and it tells them in text form, too. This
supposedly increases the user’s ability to understand thesystem’s intention even if it suffers from low tracking
accuracy. This way the user sees where they are headed
to, based on the position of the words while the words
themselves provide clear instructions in case of unclear
situations.

7

Instant Feedback

Environmental Awareness

Multimodality Count

Familiarity

Average Rank

5
2
4
3
1

2
1
4
5
3

3
5
1
3
1

1
1
5
1
1

3
1
5
4
2

2
2
2
1
2

1
3
1
5
3

2.43
2.14
3.14
3.14
1.86

Table 3: Ranking of the different display techniques(left) within the selected criteria (top), where lower numbers are better.
Table 3. A lower number means a better ranking in therespective category. The ranking of the baseline techniques has been discussed in section 5, here we only
address the performance of Bending words in comparison to these techniques.
The weighting of the criteria should be adapted basedon the goals, given environment or target group. For example when developing a solution for people with mild
cognitive impairments, the Familiarity criterion could beweighed more than Path of Information Visibility. The
exact weights would need to be determined using a user
study. For this work, we weight all criteria equally. In
many criteria, the proposed Bending Words outranks theother display techniques (Table 3), followed by WIM.

DISCUSSION

In the following we discuss the proposed Bending Words
display technique. Comparing it to the previous four display techniques, we obtain the ranking summarized in

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Lines on the ground
WIM
Digital Avatar
Haptic Feedback
Bending Words

Device Orientation

The text aligns with the navigation path in front of theuser. The action keyword Go is positioned at a small
distance from the user and rotated around the y-axis toface the user at an angle. Due to the resulting foreshortening effect this indicates a walking direction for them.
The absence of precise directions makes the user awarethat this is only an approximate guidance. The direction
keyword is placed at a larger distance and aligns withthe path similarly as the action keyword. Its rotation isadjusted to keep a viewing angle that ensures readability. The angle adapts so that the foreshortening guides
towards the next corner. When approaching a waypoint
where the directional change would indicate an upcoming corner, the direction keyword snaps to the waypoint
to emphasize this change before continuing along thepath. An example is shown in Figure 3.

Path Information Visibility

The displayed text is large enough to be easily readable
but small enough to hide as little as possible of theenvironment. To improve readability and let the text
stand from the envrionment, we opt for a black text withwhite contour.

Deviation Range

Figure 3: Bending Words: From left to right: The text aligns roughly with the path and the foreshortening as well as
the content guide the user. If a new waypoint, e.g. corner, is approached the direction keyword changes and snaps to
the waypoint’s position. Once the waypoint is passed the technique returns to it’s initial setup but always using theforeshortening effect to gently guide the user towards the next waypoint.

143

Deviation Range: As the words in Bending Words only
hint towards the next waypoint the text supports a clear
decision making, resulting in strong robustness even

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with a rotational error of up to 10◦ ranking it the highest
among the compared techniques. While the user canalso correct any error when using WIM by comparing
the map with their surroundings, this comes at a slightly
stronger cognitive load though.

signs which resemble Bending Words. Though, we areprobably more used to follow other persons, as with thedigital avatar. However, few of us are used to interpret
vibrations as an information channel.

Strong translational and rotational errors can also havemore drastic negative effects. The less dramatic effectwould be that the system guides the user against a wall,in a more dramatic case, which we encountered during
our own tests, Digital Avatar and Haptic Feedback would
guide the user right down the stairs, even though thecorrect path continued to the right directly after the stairs.
WIM can make it hard for the user to decide for thecorrect path, as the goal is displayed in the map but
not necessarily the path. Even though the placementof Bending Words may also guide the user towards thestairs, the textual information always gives the user the
possibility to correct the presented information. E.g. if
Bending Words states “Go right” it is obvious that one
should not go down the stairs as it would state “Go down”
or even “Go down the stairs” instead.
Path Information Visibility: While Haptic Feedback
and Digital Avatar only roughly indicate the current direction, Bending Words and Lines on the ground provide
some contextual information (position of next waypoint,
direction beyond that), and WIM even reveals the wholesurrounding. We deem Bending Words to be slightly
worse than Lines on the ground with this regard but better than Digital Avatar as it provides more information
about the next waypoint earlier on.
Device Orientation: Similar to the Digital Avatar,
Bending Words enforces the desired upright orientationof the tracking device. All other techniques implicitly
enforce a worse orientation towards the ground.

Limitations: Within our study we did not investigate
how display techniques could be combined to improve
the shortcomings of each other. For example, the Digital
Avatar could be combined with an indicator that tells
the user where the avatar is currently waiting for them,or the non-visual Haptic Feedback technique could be
combined with a visual technique for a similar effect.
Bending Words is also limited by its reliance on turnby-turn instructions, which can have adverse effects
on spatial learning [KAS]. Its utilization of 3D space
imposes an additional constraint on the number of words
that can be displayed, further limiting the amount of
instructive information that can be conveyed.

8

CONCLUSION

This article has presented a set of seven objective evaluation criteria for error-robustness in AR indoor navigation.
Based on these, we have introduced a new display technique called Bending Words that can reduce the impactof tracking errors within an AR navigation application.
We have evaluated and compared it to four other baseline techniques. Bending Words ranks best within thecriteria, closely followed by WIM. Bending Words expands the spatially registered information provided byan AR display with precise instructions that can be easilyinterpreted.

Instant Feedback: Bending Words constantly reflects
updates from the tracking system and quickly corrects
known errors. Therefore, all techniques, except for Digital Avatar, perform equally well.
Environmental Awareness: An active process of
localization supports the learning process of spatial
knowledge required to independently navigate the building [Kui16]. Bending Words provides a neat way to give
just enough guidance to stay on track, supporting memorization of the path. Therefore, we rank it slightly better
than Lines on the ground. While we could give more
precise context information, e.g. "Go right at elevator",
this might clutter up screen space. Such extensions toour basic approach should be evaluated in the future.
Multimodality Count: As all techniques, except for
Haptic Feedback, Bending Words mostly focus on the visual sense. It would be very simple to include additional
senses, though, e.g. through audio feedback.
Familiarity: One can argue that we are used to textual instructions e.g. from assembly manuals or street

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WSCG 2023 Proceedings

144

In the future, new display techniques could be constructed for each visualization category, using the criteriapresented in this work. It would be interesting to see
how these criteria and other human factors interact witheach other.

9

ACKNOWLEDGMENTS

Partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) –491805996 / GR 5932/1-1.

10

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