Displaying landmarks and the user's surroundings in

Displaying landmarks and the user's surroundings in indoor pedestrian navigation systems Christina Ohm

∗,

Manuel Müller and Bernd Ludwig

Chair of Information Science, Universität Regensburg, Universitätstraÿe 31, 93053 Regensburg, Germany E-mail:{christina.ohm, manuel-tonio.mueller, bernd.ludwig}@ur.de

In this paper a holistic approach for developing indoor pedestrian navigation systems is described: rst of all, a map modeling toolkit is introduced that allows for the simple and fast creation of environment models and calculation of preference-based routes in various indoor areas. Furthermore, it is shown that landmarks can be easily derived from this model. The landmark selection is based on three user studies that show that "functional" landmarks like doors and stairs are suitable for navigation. The main study was conducted with 64 participants to evaluate dierent depictions of the user's surroundings including landmarks. For this purpose an abstract graph-like navigation prototype that uses the data of the modeling toolkit was compared to a depiction additionally showing a mobile map. Results indicate that especially users with a good sense of direction perform signicantly better with the graph-like interface in terms of task completion time. Abstract.

Keywords: Pedestrian navigation system, Landmarks, Routing, Preferences, User evaluation

1. Introduction

With the increasing computational power and popularity of mobile devices in recent years, a diverse range of dierent user interfaces to pedestrian navigation systems become feasible [19]. These systems were developed alongside with car navigation systems, but it would be a naive assumption to simply apply interaction techniques borrowed from state of the art car navigation instructions to pedestrian navigation systems. Pedestrians prefer route instructions based on landmarks instead of metric statements like "Turn left in 100 meters". This results from the fact that pedestrians have weaker capabilities in measuring * Corresponding author. Address: Universität Regensburg, Universitätstraÿe 31, 93053 Regensburg, Tel. no.: 0941 943-3465, fax. no: 0941 943-1954, E-mail: [email protected]

distances quickly, whereas landmarks, commonly referred to as salient objects in pedestrian navigation and waynding literature, can solve this problem [28,39,51,52]. Furthermore, pedestrians have more degrees of freedom: they are not bound to streets like automobiles and navigation can take place in outdoor and indoor environments [48]. Moreover, they can "use" objects like stairs, elevators or trac lights that can help them while navigating. Whereas a lot of pedestrian navigation system prototypes were developed for outdoor environments (see Section 2.3.1), there is still a lack of scalable solutions for indoor areas even though people tend to get lost more easily in buildings [6]. This gap results probably from the fact that localization techniques like GPS as well as comprehensive environment models covering a diverse range of indoor areas are not available. In the present

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C. Ohm et al. / Displaying landmarks and the user's surroundings in indoor pedestrian navigation systems

paper, this gap is address by posing the following research question: How can indoor environments be modeled in

order to calculate preference-adapted routes and assist in navigation?

For this purpose a map modeling toolkit was implemented that allows fast and easy creation of environment models. These can be used to calculate routes that are adaptable to personal preferences (e.g. avoidance of stairs in case someone has to carry a lot of luggage or is disabled). However, after the best suited route for each pedestrian has been calculated, one signicant problem remains: landmark-based pedestrian navigation is only feasible if a substantial set of salient objects is available for a lot of dierent environments like hospitals, airports or other public buildings (e.g. universities). Therefore, the environment models have to be enriched with landmarks, such that the derived data is sucient to create a basic and scalable pedestrian navigation system. Accordingly, a second research question was addressed: Which landmarks can be used in indoor en-

vironments? Consequently, which data has to be added to the environment models?

In order to nd answers to this question, three user studies on the selection of landmarks in indoor environments, i.e. a university building, a shopping mall and a train station, were conducted. Participants were asked to accomplish a distinct route and meanwhile name salient objects. Afterwards, the most important landmarks were identied, whereas it was additionally investigated whether they are part of the environment models or can be easily added. Finally, with environment models, routes, and landmarks for indoor pedestrian navigation systems available, the most important question emerges: How should the routes enriched with land-

marks be displayed?

This problem is twofold: rst of all, the surroundings of the user have to be depicted on the screen, so that he or she can self-locate him or herself at the current navigation point. Secondly, it is essential to choose appropriate landmarks from the set of salient objects gained from the environment models. Therefore, another user study was con-

ducted in order to analyze how interfaces of pedestrian navigation systems should be designed. More precisely, a very abstract interface design, only presenting the current route segment and landmarks, was compared to a depiction additionally using a digital map. Although it is state of the art to display digital map representations in indoor environments (see Section 2.3.2), the study was based on the consideration that available map material provides information that is not immediately necessary during the waynding task while a mobile navigation system is used. This hypothesis is additionally based on the studies of [7,37], who argue that a reduction and abstraction of the interface can minimize cognitive load and enhance navigation eciency. For the study presented in this paper oor plans provided by the administration devision of the University of Regensburg were evaluated against an abstract interface design. These maps are currently oered online as navigational aids to visitors and students. Furthermore, the plans are installed within the buildings and serve as "You-arehere" maps, e.g. in case of emergency. It is argued that this map material is useful for waynding, but does still include too much unnecessary information for the use in an mobile indoor pedestrian navigation system. On account of the questions presented above the remainder of this paper is structured as follows: First of all, an overview of the related work concerning graph modeling, the selection of landmarks, and pedestrian navigation systems is given. Section 3 reports on the implemented modeling toolkit. Subsequently, the user studies investigating which landmarks can be used for navigation is presented. In Section 5 the main user study concerning the depiction of route instructions is specied in detail and the results are discussed. Finally, the ndings of the studies are summed up and their implications for the design of (indoor) pedestrian navigation systems are outlined. 2. Related work

As stated in the introduction, the work presented in this paper aims at the development of indoor pedestrian navigation systems and therefore three major research questions arise:


How can indoor environments be modeled in

order to calculate preference-adapted routes and assist in navigation? Which landmarks can be used in indoor environments? Consequently, which data has to be added to the environment models? How should the routes enriched with landmarks be displayed? Consequently, the following subsections describe related work that reports on environment and graph modeling. Moreover, studies and models concerning the selection of landmarks are presented. In this context, the concept of landmark saliency is briey introduced. Finally, studies that aim at the implementation and evaluation of outand indoor pedestrian navigation systems are presented and their implications for the main study described in Section 5 are outlined. 2.1. Graph modeling

Generally, indoor environment models can be divided in dierent groups (see [53]): there are topological models, essentially including information about the connectivity between dierent locations. Semantic models that investigate the properties and relationships of existing entities within dierent environments form another group. For instance, [1,12,54] developed semantic ontologies for indoor environments. This work can provide information regarding the entities that have to be taken into consideration for preference-based routing. Nevertheless, the focus of the work presented in this section lies on hybrid models, taking into account topological and geometrical data: the former contains information about accessibility that can be used for the calculation of routes. The latter can assist the users in the process of navigation. In principle, attempts to navigate in indoor areas based on the models of OpenStreetMap [33] seem promising. Since modeling the environment is rather time consuming, crowdsourcing this task could ensure the availability of comprehensive, updated models in the long term. Link et al. [23], for an instance, implemented an indoor navigation system for wheelchair users with maps from OpenStreetMap. However, the tagging schema of OpenStreetMap, which was initially developed to provide "good-looking" maps, had to be extended and corresponding models had to be created in the respective areas [34,15].

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Aside from OpenStreetMap, numerous concepts for the design of indoor environment models exist: IndoorGML [22] is an application schema of OGC Geography Markup Language (GML) [14]. Although it especially focuses on indoor navigation, information about the spatial geometry can also be included, which makes IndoorGML useful in various elds of application. In Lorenz et. al. [24] and Ohlbach and Stoel [32] an environment model that allows for the calculation of routes was developed. Additionally, the generation of helpful instructions while navigating was examined. In addition to the denition of general environment models, the development of map modeling tools is an topic in several related research projects: Stahl and Schwartz [47] implemented a toolkit that allows the creation of threedimensional indoor models. Beyond the providence of navigation, sensors that help to position the user, can be added to these models. Combining navigation and positioning was also intended by Ruppel et al. [41]. Their platform Indooria assists in the creation of topologies for buildings with particular focus on indoor location based services. Also worth mentioning are approaches that generate environment models in a more automatic way, as this could help to simplify the time consuming process of manual map modeling. Kim et al. [17] introduced a framework for capturing three-dimensional room models with the help of a smartphone. Their models were not developed for the sake of route calculation, but to assist the user with realistic depictions of the environment. This can be advantageous, for example, while visiting an art gallery. There are also a lot of attempts to convert blueprints to environment models. Yin et al. [55] give an overview of dierent approaches to automatically generate 3D models from architectural plans. For the approach followed in the present paper, however, an automatic conversion of map material seemed unsuitable. Apart from the fact that this dicult eld of research is still confronted with problems, available oor plans dier signicantly: for instance, three environments were modeled, i.e. a university, a shopping mall and several train stations described in Section 3. These models were also used for the three pre-studies (see Section 4): for user study one in the campus of the university raster graphics of oor plans existed. For the

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second user study in the shopping mall, no oor plans were available, so that a perspective illustration had to be used as a basis for modeling the building. Finally, for the third study, architectural plans were available for several subway stations, but these diered a lot in terms of style. Moreover, for a few train stations no map material was accessible at all. Although the adaptation of the OpenStreetMap format seemed promising, it was too complex within the scope of this research, since the buildings, in which the navigation should take place, were completely missing in OpenStreetMaps. Thus, enormous eorts to model these building in OpenStreetMap would have been necessary. Consequently, a new format was created, especially tailored to the needs of the research objectives, while keeping in mind that it would be reasonable to reserve the possibility to transform the environment models into a more common standard in the future. 2.2. Landmark selection

Referring to salient objects is considered to be the most eective way to communicate any navigation instruction in human navigation. As stated in the introduction, it is a well-proven fact that pedestrians prefer navigation instructions based on landmarks in both, out- and indoor areas [16,27,28,39,51,52]. Moreover, researchers discuss to include landmarks in car navigation systems (see e.g. [5]). All in all, some work concerning landmark selection addresses the question, how the salience of an object can be formalized: usually, a landmark's salience is considered to derive from its visual, semantic and structural properties, accompanied by its advanced visibility [45,51,52]. In particular, [52,39] propose a model to compute a facade's salience, taking into account its area, shape, color, as well as visibility, and additionally assigning weights to these properties. In the work of Winter [51] a formula for the calculation of the advanced visibility of an object is introduced and evaluated, whereas information about the visible area of the current navigation point is needed. Cadu and Timpf [8] argue that the salience of an object is the result of a triangular relationship involving the observer, i.e. the pedestrian search-

ing for an object, the environment and the geographic features of the navigation scene. Therefore, they identify perceptual, cognitive, and contextual salience of an object. Perceptual salience refers primarily to visual features such as shape or color, while cognitive salience depends on the observer's experience and knowledge. Finally, the respective type of navigation task reected by the concept of contextual salience is taken into account. A formalization of this model was proposed by Röser et al. [42], who additionally include visibility based on the position of the pedestrian. They identied that at decision points at which the user has to go left or right, landmarks at the left respectively right hand side are more likely to be chosen. Similar ndings are reported in [25]. Even though the models described above are very comprehensive, most of them require detailed information about the particular landmark such as shape or color. According to the work presented in [25], it is argued that the object category can contribute to the decision, whether a landmark should be used in an indoor pedestrian navigation system. A model for salience calculation was developed that takes into account the position of the object relative to the route, i.e. the structural salience, the category of the particular object, and a potential salience rating of a user. Therefore, if the route and potential landmarks at a particular decision point are known, but no rating of a user is available, the category can serve as a rst decision rule for landmark selection. A similar approach to identify landmarks in outdoor environments by taking into account their category (e.g. hotels, parks) is presented in [11]. In existing indoor pedestrian navigation prototypes the selection and use of landmarks is either not the research focus, or done subjectively by the associated researchers (see e.g. [26,49]). There are also approaches that use the above-named models of landmark selection presented in [52,39]: for instance, Cuayahuitl and Dethlefs [10] apply the proposed model in a dialog-based indoor navigation system. It has to be noted that the presented models were mainly evaluated in outdoor environments. However, strong evidence exists that people tend to get lost indoors more easily than outdoors [6]. Thus, indoor areas are an application eld that requires further research. Moreover, indoor envi-


ronments dier from outdoor areas: Brunner and Radoczky [6] argue that instructions require a higher density of landmarks. Since indoor routes usually contain more turns of directions and thus the lines of sight are shorter, a particular landmark is only visible for a short period. At the same time, a smaller choice of landmark categories compared to outdoor areas is allocatable, despite the high diversity of distinct objects [6,38]. Since the research presented in this paper is mainly supposed to support indoor pedestrian navigation, it is argued that more user studies focusing on the selection of indoor landmarks are needed. Therefore, three surveys on landmark selection were conducted that apply a user-centered approach to collect landmarks in indoor environments. The overall aim of these studies was to examine the suitability of an object to serve as a landmark depending on its category. 2.3. Pedestrian navigation systems

Over the past 15 years a lot of research has been conducted concerning the interface design of pedestrian navigation systems. Most of the work was at rst done in outdoor areas, most likely because localization information like GPS is not available indoors. Localization techniques themselves are a research topic on its own right and therefore not reported in this paper. The next subsections report in detail about both, outdoor and indoor studies, since a lot of the ndings for outdoor navigation systems can be relevant in indoor areas as well. 2.3.1. Outdoor pedestrian navigation systems

In outdoor areas, most of the studies evaluate mobile map representations against other navigational aids. In particular, one main line of research is to compare map interfaces against photorealistic depictions. Kray et al. [20] conducted a study in a city center using either a digital map representation, directional arrows enhanced with landmarks or prerendered three dimensional virtual reality views of the current navigation scene. In this study mainly street names served as tentative landmarks. Participants accomplished the route faster with the two dimensional map, even though they stated that the 3D interface was "more fun" to use. Both,

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the map and the three dimensional presentation were received as cognitively demanding. Consequently, the authors conclude that arrows enriched with landmarks can be considered as an alternative interface type, since this depiction is easy to understand and leaves out "unnecessary" information. According to this consideration, arrows enriched with landmarks were included in the interface design of the main study (for an overview see Figure 6). In an early study of Münzer et al. [31] four dierent kinds of navigation aids were evaluated against each other in a rural outdoor area. One visualization contained photographs of the current scene and additional context information, whereas another one showed a map. Furthermore, the survey included a prototype using auditory instructions supplementary to the route photos, either with or without context information relative to the route. Landmarks were only implicitly included by showing pictures of the scene. The study revealed that the digital map representation outperformed the other very elaborate interfaces in terms of navigation errors and gain of survey knowledge. Partala et al. [36] evaluated three dierent user interfaces, including a "traditional" map, a photo realistic satellite map and a photo realistic street level view. Once again, landmarks are only implicitly included by showing pictures of the current navigation scene, i.e. buildings along the streets. The authors come to the conclusion that photo realistic interfaces provide a better user experience than traditional maps (similar to [20]), even though they cause more workload during navigation. Additionally, the usability is rated signicantly lower compared to the mobile map interface. However, the street level view and thus the most realistic depiction helps to identify landmarks best. Taking the ndings of [31,36] into account, an abstract interface design was compared to a mobile map representation instead of depicting photographs of the landmark in the main study reported in Section 5. However, it is topic of further research, if the abstract representation could additionally show pictures of the landmarks in order to help the user during the waynding task. In the study of Walther-Franks and Malaka [50] a mobile map view was compared to an augmented reality interface. Pictures of the current scene enhanced with arrows that indicate the direction to take were received as more useful than the mobile

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map representation. An analogous study was conducted a few years later by Rehrl et al. [40]. Other than in [50], the augmented reality view did not include directional arrows, but gave visual hints at landmarks by using circular markers. Participants performed signicantly better with the mobile map interface in terms of workload and completion time. Augmented reality interfaces seem to be promising in terms of user experience. However, they tend to cause cognitive load and decrease navigation eciency and are therefore not part of the main study of this paper. Chittaro and Burigat [9] compared three dierent visualizations: maps, photographs with directional arrows and arrows without further information. No signicant results were found examining navigation errors and perceived subjective usefulness, yet the orientation time is signicantly higher for the map interface at the beginning of the navigation task. This results give a strong hint that map interfaces are cognitively demanding and therefore other interface designs should be taken into consideration. In another line of research, some work deals with variations of mobile map representation. In Smets et al. [44] the eect of the orientation "north-up" vs. "head-up" is analyzed in detail. The experiment was conducted in a virtual environment and showed that the participants preferred the "headup" view in this setting. This idea was further developed in the interface design of the main study. The oor plans aligned to the users walking direction, so that the map was oriented "head-up" at any time of the experiment. In Stark et al. [48] two dimensional city maps were enhanced with four dierent instruction types, i.e. displaying the route in the map, additionally giving audio instructions, only showing the rough direction to the destination, and a textual description using street names. It turned out that dynamically displaying the route on the map causes less stops and detours. Except for street names no landmarks were included in the studies investigating variations of mobile maps. The implication of this results for the map design of the main study is to depict the exact route and not only the rough walking direction.

2.3.2. Indoor pedestrian navigation systems

While the studies mentioned above were conducted in outdoor environments, similar but less extensive research was done in indoor areas. In particular, some studies examine the suitability of dierent mobile map representations. For instance, Bouwer et al. [4] implemented a prototype for a fair. Even though they found out that participants eectively reach their destination with their UI, they argue that people have problems to initially orient themselves at the beginning of the navigational task and that salient objects (i.e. the stands of the fair) cannot be identied by the test persons. These ndings are consistent with the results of [9] in outdoor environments and were also conrmed by a very similar study of Puikkonnen et al. [37] conducted in a shopping mall. Consequently, [37] also recommended to simplify digital map representations for indoor navigation to reduce the cognitive load of the user. In both indoor studies landmarks were only implicitly included by referring to shops in the mall or stands of the fair. Taking into account that users in these studies searched for more landmarks to orient themselves, explicit and extensive use of landmarks for navigation is advised. Butz et al. [7] discussed the use of dierent visualization techniques dependent on currently available data about localization accuracy and orientation information. They propose a simple directional arrow when localization accuracy is high and suggest to use a two dimensional map in case of decreasing accuracy. Furthermore, graphical abstraction to reduce computational load and focus the attention of the user is suggested. The observations of the studies presented above form the foundation for the abstract and reduced interface design used in the main study. It shows a graph and explicitly references to landmarks. Another line of research in indoor areas once again examines mobile map presentations, but rather to examine these depictions on their own, other, mainly three-dimensional visualization techniques are also included. Obviously, this is again very similar to the large amount of outdoor studies examining the same topic. For instance, Taher and Cheverst [49] compared mobile map representations to three-dimensional depictions of the environment. Their results show that salient objects are more likely to be used as reference points for navigation if participants used


the three dimensional interface. Still, test persons stated that they would prefer to have the option to see the map at any point of the navigation task. In Fontaine [13] oor plans, axonometric representations depicting the detailed structure of the test environment, and "frontal view" maps were provided to participants printed on paper. Results show that test persons gain a better mental representation of the vertical relations using the detailed axonometric depictions. As already outlined in the previous section on outdoor studies, depictions showing detailed and perspective views such as augmented reality interfaces or axonometric representations tend to cause cognitive load. Furthermore, the implementation of such interfaces requires the acquisition of a large amount of data. This is quite contrary to an approach that aims at the implementation of an reduced interface that shows only the minimum of information necessary for waynding. Therefore, the system design presented in this paper is scalable to large environments and congurable for new areas. Some studies in indoor environments focus on the question how textual navigation instructions should be structured and which landmarks ought to be included. For instance, Mast et al. [26] compared a stepby-step guidance to textual route instructions taking into account a whole navigation scene. Their results show that more elaborate textual route instructions are perceived as more helpful. However, these instructions were verbalized by the researchers and can therefore not be generated automatically. Furthermore, the selection of landmarks is rather unstandardized. To overcome the problem of landmark selection, several pre-studies were conducted that are described in detail in Section 4. In Bigler et al. [3] a very simple graph-like map was compared to textual instructions. Participants got their instructions printed out on one page, therefore no "real" pedestrian navigation system was included. Yet, since the sketch map was very similar to the approach presented in Section 5, this study is important related work. Particular focus was drawn on level changes. It was noted how many errors or hesitations occurred and if the task was experienced as dicult by the participants. The results showed no dierence for the two interfaces concerning these dependent vari-

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ables. Consequently, the conclusion is drawn that a pedestrian navigation system should provide both sketch maps and textual instructions to support the waynding task. Therefore, the reduced depiction style of the sketch map was adapted and combined with textual instructions for the study presented in this paper. 2.3.3. Discussion

To sum up the related work, mobile map representations for pedestrian navigation systems have been primarily evaluated against very elaborate interfaces like virtual depictions of an intersection or augmented reality interfaces. On the other hand, the majority of outdoor studies has shown that mobile map representation cause less workload and participants accomplish the route faster with the "traditional" navigation aid, even though e.g. augmented reality applications are "more fun" [20,31,36,40] to use. Similarly, in indoor areas [49] have shown that participants prefer to be able to choose whether they can see a mobile map at any point of the navigation task. Since main areas of application of the project presented in this paper are train stations, where travelers have to reach a train in time, or universities, where students have to hurry to attend a lecture, the prototypes described in the main study do not include augmented or virtual reality navigation interfaces that create cognitive load. In addition, concerning such complex UIs, the question of scalability arises. Even though such interfaces seem to be more pleasurable, how can they be implemented for a wide range of dierent indoor areas like university buildings, hospitals and airports? As argued in the next section, the toolkit used to develop environment models for the navigational prototypes presented in the main study is easy and fast to use and applicable in any environment. In order to address the third research question, i.e. how routes enriched with landmarks can be displayed, the idea of abstracting the depiction of the user's surroundings of [7] is extended for the main study: a graph-like representation directly derived from the modeling toolkit (see e.g. Figure 4) is used to guide the pedestrian. The research presented in this paper also continues along the path of [9], who, despite the advantages of map presentations, argue that it takes the user more time to orient him- or herself at the beginning of

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the navigational task. Also [37] recommend to simplify the graphical layout to avoid unintentional blindness, which means that users focus on one interface element, i.e. the map and unintentionally ignore other UI elements like icons. 3. Modeling toolkit

In this section the modeling toolkit is presented, that was developed to address the rst research question, how indoor environments can be modeled in order to calculate preference-adapted routes and assist in navigation. Essentially, the pedestrian navigation system described in this paper aims at the use in three dierent types of environment: A campus navigation system that shall help

new students to nd their way to lecture halls, oces or labs. A navigation system that leads customers through a large shopping mall. A system that assists users in nding their way at train, subway, tram, and bus stations. In this context, the pedestrian navigation system is one part of a journey planner application for the local public transport services. Primarily, the navigation was intended to assist users in indoor areas, but nevertheless outdoor areas had also to be covered by the models. This was required to provide navigation between dierent buildings, e.g. at the campus of the modeled university. No external outdoor navigation service could be used to navigate between the buildings, since available maps were far too inaccurate. An important aim in this research was the adaption of calculated routes to individual preferences of various users. Therefore, options like the avoidance of stairs, revolving doors, or outdoor areas had to be considered. For the project described in this paper, an environment model was needed that was capable to fulll the following requirements: All possible destinations had to be registered

and associated with meaningful descriptions, which can help the nal user to identify and select these locations. Footpaths had to be allocatable: to calculate preference-adapted routes, the length and the

Fig. 1. An simple example of a graph modeled with the introduced toolkit.

type of footpaths had to be available. As an example, it is essential to know, if a pavement is made out of cobblestone, since such kind of footpath is hard to access with a wheelchair. Preference-adaption required to dierentiate diverse door types. It is dicult, for instance, to pass a revolving door with a baby carriage. Dierent types of oor changes like "stairs", "elevators" or "wheelchair accessible ramps" had to be registered. In order to localize the user in the environment, it had to be possible to assign geocoordinates to the model. The developed navigation applications had to be set up in many dierent environments. The navigation system had to operate not only in one single building, but also in arbitrarily large areas. Therefore, scalability had to be ensured. A large amount of buildings had to be covered, so the modeling process has to be fast and easy.

Due to the mentioned considerations, a very simple approach was pursued. Instead of taking walls or other architectural elements into consideration, essentially only a topological graph is modeled, which is the basis for calculating routes. The structure of this net will be described in the following. Figure 1 illustrates an exemplary graph and provides examples of the mentioned elements.


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Fig. 2. Screenshot of the mapping toolkit while modeling a typical university building.

First of all, the terrain is split and environment models of the partitioned areas are stored in different XML les to ensure scalability. Each XML le contains nodes that are mainly used to model simple intersections, indicated by red dots in Figure 1. In addition, these nodes can represent other objects from a dynamically adjustable list, like different kind of doors, oces or bus stations. Applying weights to the dierent types of nodes plays an important role for the calculation of preferenceadapted routes. Besides that, a node can also serve as a landmark during navigation depending on its type, contextual position, etc. The various types of the nodes are illustrated by small icons. Figure 1 contains, for instance, several doors, a parking lot, and an oce. The nodes are connected by edges that represent possible paths between them. Dierent tags, which are primarily used for preference-adapted route calculation, can be assigned to these edges. In this way, edges can be marked as, for instance, street crossings, as lying in an outdoor area, or as leading through an oce. Dierent colors are intended to visualize these edge types. The orange edges in Figure 1 symbolize paths through outdoor

areas, the yellow edges represent paths through ofces, etc. A special group of edges forms connections between dierent oors, like stairs, elevators, ramps, or escalators. Again the types are illustrated by icons, the direction (up or down) is accentuated by color and orientation. Taking into account that escalators going upwards in shopping malls are often located separately from escalators going downwards, these "level-edges" are generally unidirectional connections. Finally, the various XML les can be connected among themselves by links. Since the structure of such XML trees is very complex, a direct editing of these les is nearly impossible. The developed mapping toolkit shown in Figure 2 and described in more detail on in [29], provides functionality to assist users in the modeling process. The environment models are hosted on a central server, so they can be modied just using a web browser. In the center of the interface the graphic representation of an XML le is displayed (see Figure 2). Users can zoom and shift this view, they can insert or modify nodes, edges or level-edges by simply clicking on the display, which can optionally

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be overlaid with raster graphics of oor plans. All changes that are made in this view are instantly integrated in the XML tree and vice versa. The experiences with the tool as well as the requirements and results of various studies led to numerous extensions that were added to the functionality of the mapping platform. One important issue is the calculation of geo-coordinates. For several applications, it has to be possible to convert the position of nodes in the environment model to geo-coordinates. This can, for instance, be helpful to display routing graphs in external maps. Moreover, it allows to nd transition links to external outdoor navigation services or to provide heuristics that can be used for the fast calculation of routes. Likewise, geo-coordinates have to be transferred to the coordinate system of the environment models to determine the current position of a user. In order to provide this functionality, nodes with known geo-coordinates can be placed in the map models. If at least three of those nodes in one XML le have been created, the mapping tool will calculate a transformation matrix. This is done by optimizing for a local minimum of the squared error between transformed and given coordinates. Utilizing this method, geo-coordinates can easily be obtained from pixel coordinates. By transforming the user's position with the inverse matrix, the user can be located in the environment model. Although this procedure ignores the curvature of the earth, the error of this approximation can be kept low, since dierent matrices are calculated for distinct and relatively small areas. As mentioned above, buildings and surroundings of the university, a shopping mall and a train stations were modeled with the introduced toolkit. This data was used for the studies described in the next sections.

4. Pre-studies on landmark selection

In order to address the second research question, i.e. which type of salient object can be used in indoor environments, three pre-studies following a user-centered approach to collect landmarks were conducted. The next sections describe the experimental set-ups and the results.

Table 1 Test route characteristics of environments. (1) University (2) Shopping mall (3) Train station

the

dierent

potential landmarks 258 92 33

study

length in meters 400 270 170

Table 2 Participants of the pre-studies. (1) University (2) Shopping mall (3) Train station

#participants (female) 34 (18) 23 (8) 17 (4)

mean age (sd) 22.5 (2.4) 24.0 (4.5) 21.8 (1.5)

4.1. Experimental set-up

The studies were carried out in three dierent test environments (see table 1). The rst study was conducted in the rather complex indoor environment of the university of Regensburg. This test route was as well used for the main study (see Figure 7). The second survey took place in a largescale shopping mall. Finally, the selection of landmarks within a train station was examined. For each route dierent test persons were recruited. An overview of their demographic data is given in table 2. The participants had to accomplish the route twice. At rst, they followed the test supervisor, who did not give any route instructions, so that the test persons could gain spatial knowledge. This is considered to be crucial for the ability to select appropriate landmarks [27]. The participants were told to look closely at their surroundings, since they have to remember objects that can be used to describe the route afterwards. This procedure results in the test person's attention to be directed to salient objects that can serve as landmarks in route instructions. According to the experiment design to collect landmarks by Sefelin et al. [43], the participants named salient objects as though they would explain the route to a stranger in the second run. This data was collected using audio recordings additional to written records. Subsequently, potential landmark candidates like elevators, escalators, stairs, doors, plants, shops, information boards, and signs [6,38] were identied manually in every test environment (see e.g. Figure 3). This included basically all objects


along the test routes, except for the oor and the ceiling. Finally, each object was assigned to one of ve landmark categories: Architecture (Arch): Pillars and fronts Function (Func): Doors, stairs, and elevators Information (Info): Signs and posters Furniture (Furn): Tables, chairs, benches, and vending machines Shops (Shop): Restaurants and shops

As a nal step for each test environment, it was manually assigned to each object, whether the participants selected it for their route explanation. 4.2. Results

In the university 953 landmarks were selected in total: as illustrated in the rst two rows of table 3, test persons mainly selected functional objects. This does not primarily result from the fact that more functional objects are located along the test route: row three indicates that the probability of a user to select a particular functional object is 22%, which is relatively high compared to the remaining categories. As expected, participants selected mainly shops and restaurants as landmarks in the shopping mall (row 4 and 5 in table 3). However, the examination of the probability that an object of a certain category is selected reveals that this is mainly due to the fact that far more shops are located along the route. Taking this fact into account, both functional objects and shops seem to be appropriate landmarks in this environment (row 6 in table 3). The results of the selected objects in the train station are similar to those observed in the shopping mall: at rst glance, objects of one particular category, i.e. the information landmarks, seem to be preferred (row 7 and 8 in table 3). Nevertheless, the probability that an object of the functional category is selected is 43%, which is by far the highest value in this experiment (row 9 in table 3). The ndings of the pre-studies can be summed up in the following observations: a pedestrian navigation system for shopping malls using landmarks to navigate the user should denitely present shops. However, if these object types are not available at a particular decision point, functional landmarks seem to be the appropriate alternative. Moreover, the results imply that functional land-

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marks are the most suitable objects to be used in any indoor pedestrian navigation system, if they are available at a certain decision point. This is clear especially for the tested university environment. Fortunately, these objects are already part of the environment model (see Section 3). Thus, a basic set of salient objects can be derived from the models for the main study, without further adjustments or manually added landmarks. It should be noted that shops are already part of the environment models for malls, as they are possible destinations. Therefore, this landmark could also be easily included. The pre-sudies had some limitations, especially concerning the third test environment: as shown in table 1, the train station route is comparatively short (the station in the city of Regensburg is relatively small). Moreover, only 17 persons participated in this study. University buildings can dier in architectural structure and available landmarks (e.g. having more works of art or architectural objects with outstanding colors). An additional limitation it that all tests were conducted with rather young participants. Therefore, further examinations with other user groups can help to gain deeper insights. Despite all this restrictions that have to be addressed in future work, the promising results of these pre-studies built the foundation of the main study described in detail in the next section. 5. Main study on depiction of the user's surroundings and landmarks

Keeping the ndings of the work presented in the previous sections in mind, the last research question was addressed: How should the routes enriched with land-

marks be displayed?

For this purpose, two prototypes for pedestrian navigation were implemented using the modeling toolkit and a set of landmarks according to the results of the pre-study conducted in the university. Even though it is analyzed whether the chosen landmarks are of help for navigation, the focus of the main study is especially on the question how the user's surroundings should be depicted.

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Fig. 3. Exemplary scenes with landmark candidates for the test environments university, shopping mall and train station (from left to right). Table 3 Observations of selection separated by dierent landmark categories: total number of selected objects for every category (1,4,7), probability that a selected object belongs to a certain category (2,5,8) and probability that an object of a certain category is selected (3,6,9). Observations / Category (cat)

Arch

Func

Info

Furn

Shop

P

(1) (2) (3) (4) (5) (6) (7) (8) (9)

142 0.15 0.07 0 0.00 0.00 9 0.07 0.10

549 0.58 0.22 26 0.18 0.20 26 0.20 0.43

131 0.14 0.07 5 0.04 0.02 85 0.66 0.19

131 0.14 0.08 6 0.04 0.03 0 0.00 0.00

105 0.74 0.21 8 0.06 0.11

953 1.00 142 1.00 128 1.00 -

University #(selected objects in cat) University P (cat|sel=true) University P (sel=true|cat) Shopping mall #(selected objects in cat) Shopping mall P (cat|sel=true) Shopping mall P (sel=true|cat) Train station #(selected objects in cat) Train station P (cat|sel=true) Train station P (sel=true|cat)

For this purpose an abstract interface design only showing landmarks and the current route segment is compared to a depiction additionally showing map material provided by the administration division of the university. As stated in the introduction, these maps are currently oered online as navigational aids to visitors and students. Furthermore, they are used as "You-arehere" maps within the university buildings. Consequently, they follow the typical design principles for this type of maps (e.g. discussed in [18,21]). Even though this map material provides a good guidance when used as a paper map, it is argued that it still includes too much unnecessary information for the use in a mobile indoor pedestrian navigation system. One of the main criteria for good map design is completeness, i.e. the plan provides all information necessary to fulll the task [18]. It is argued that interfaces of mobile pedestrian navigation systems require less graphical information to support the waynding process than

cat

oor plans that serve e.g. as "You-are-here" maps. Beyond this, following the ideas of [7,37] a reduction of the map interface can enhance navigation eciency and is therefore evaluated in the presented study. Consequently, the following hypothesis was devised specifying the research question of the main study: H1 : An abstract graph-like representation can

support waynding better compared to a depiction additionally showing a mobile map.

The following subsections report in detail on the experiment design as well as the results of the main study. 5.1. Interface and system prototype

Basically, the indoor user interface uses the output of the modeling toolkit that allowed to map the whole university and calculate routes through


Fig. 4. Exemplary screenshot of the "Map"-prototype. Participants received all instructions in German. The interface elements are translated in English for the reader's convenience.

the buildings. The two system prototypes were implemented for Android smart phones and consist of four interface sections. As mentioned before, the main dierence is the depiction of the user's surroundings: the graph-like system shows nothing else than the current route segment - including landmarks (see the illustration in Figure 5 label 3). On the other hand, the map interface depicts the respective oor plan (see Figure 4 label 3). The map material was provided by the technical and administration division of the university of Regensburg and was not further edited. The landmark for each scene is displayed using an icon labeled with the name of the particular object (see e.g. Figure 5 that shows the landmark "door"). The current position of the user is indicated with a green manikin. In contrast to the other screen elements, the map respectively graph depiction aligns to the walking direction of the user, which is based on the compass data of the smartphone. Both interfaces additionally depict two sections at the top of the display. In section 1, a textual navigation instruction is provided that includes a statement concerning the direction to take, i.e. left, right and ahead. The text additionally refers

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Fig. 5. Exemplary screenshot of the "Graph"-prototype.

to a landmark for each step of the route. For instance, both instructions in Figure 4 and 5 say: "Go straight ahead and turn right in front of the

While preparing the main study the text instructions could not be generated automatically by the system. Therefore, they were formulated by the test supervisor in accordance with the xed "direction + landmark" pattern. [20] state that arrows enriched with landmarks can be of help for the user, since they are "immediately relevant to the current task". Therefore, arrows are included in both prototypes (see Figure 4 and 5, label 2). Landmark are displayed using an icon positioned relatively to an arrow according to the current route segment. If the user was supposed to go through a door or e.g. up the stairs, the landmark icon was positioned at the arrow head. All in all, a set of 13 dierent arrowlandmark combinations was used (see Figure 6). Neither the text instructions nor the landmark enriched arrows did dier amongst the user groups and are therefore controlled variables. The evaluation of both, the arrow design, and the text instructions is a topic of further research. Since no localization was available for the experiment, two buttons are positioned at the bottom of the screen (label 4). They are labeled with "Next" (right button) and "Back", so that the user had to decide him- or herself when the next routing indoor."

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Fig. 6. Set of directional arrows used for both prototypes with assignments to the 23 navigation steps.

struction should be displayed. To ensure that the participants realized when they were supposed to press the "Next"-button, they were precisely instructed before the experiment (see Section 5.3). 5.2. Participants and devices

Students of an undergraduate course in software ergonomics were precisely instructed to recruit and supervise four test users each for the study described in the following. This resulted in a test sample of 72 participants. Eight of them had to be excluded from the study due to technical problems or wrong instructions. For the remaining participants, the mean age was 24.02 years with a standard deviation of 7.9 (range:11-64). 33 male and 31 female persons participated in the study, most of them being students. The tests were conducted with a Samsung Galaxy Tab 3 10.1, so that the test supervisors could examine the actions of the participants at any time, since the used 10 inch screen is comparatively large. 5.3. Experimental set-up

The test route of the pre-study conducted in the university building was used (see Section 4) and divided into 23 steps with one landmark per step. Taking the position of the salient objects into account, each step was approximately of equal length. Figure 7 shows the location of the selected

landmarks. In addition, it illustrates how many of the 34 participants of the pre-study have chosen the particular object to describe the route. Furthermore, the branching factor, i.e. the number of possible walking directions at each decision point is annotated to every step. In Figure 8 pictures of all decision points with a branching factor higher than 3 show the particular situation. At decision points with a branching factor of 2 a navigation instruction had to be given, since the user had to go through a door or the landmark of the next decision point was not visible at the particular scene. The set of used salient objects is a result of the pre-sudy, and therefore contains mostly doors and stairs. As an exception, landmarks 10 (cafeteria) and 16 (toilet) refer to permanently installed rooms. Further exceptions are landmark 3 (pillar) and 11 (no landmark): in these scenes functional or salient objects were missing. The last landmark was the destination of the test route. In this situation, several similar doors are visible (see Figure 16). In order to disambiguate this situation the room number (in this particular case the room "name", i.e "Information Center") was displayed. This information is included in the environment models described in Section 3. A between subject design was employed, so that the participants were randomly assigned to either the map interface or the graph view.


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Fig. 7. Route of the main user study with assigned landmark type, location, branching factor of the decision point and number of users that selected the particular object as a landmark in the pre-study conducted in the university.

The experiment consisted of three parts: At rst participants were asked to ll in a form, where they, besides providing their demographical data, had to answer a German language self-report sense of direction scale presented in [30] (all participants were native German speakers). After that, the test persons were instructed how the prototypes described in Section 5.1 work. In particular, they were briefed that the system did not provide any localization information and that they had to press on the "Next"-button, if they wanted to see the next navigation instruction (see Figure 4 and 5, label 4). It was emphasized that they had to click this interface element as soon as they had reached the destination of the current instruction. For instance, when the instructions "Go ahead through the door" appeared, the button had to be pressed immediately after the persons passed the door. The time elapsed for each step was recorded as the main dependent variable. The "Back"-button beside the "Next"-button should only be pressed if the participants lost their way. Either they recognized themselves that they got lost, or they were told by the test supervisor. After the participants accomplished the route with their respective navigation prototype, they had to ll in a form. It consisted of the following questions (all verbalized in German) and had to

be answered on a 7-point Likert scale (1: strongly disagree, 7: strongly agree). 1. I was very familiar with the test route before the experiment. 2. There were enough salient objects displayed, so that I could orient myself. 3. The chosen salient objects (like doors or stairs) were helpful. 4. The directional arrows helped me while navigating. 5. The text instruction helped me while navigating. 6. The depiction of the surroundings helped me while navigating. 7. The automatic alignment of the map helped me while navigating. 8. I felt comfortable while navigating. Each of the questions 2-7 is meant to evaluate one particular interface element of the prototypes depicted in Figure 4 and 5: question 4 examines the perceived usefulness of UI element 2, which is the depiction of an arrow supplemented with the particular landmark. Question 5 aimed at the evaluation of the text instructions (label 1), whereas question 6 and 7 are intended to measure the usability of screen element 3, i.e. either the graph or the map. By asking question 2 and 3, the subjec-

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Fig. 8. Photographs of the decision points with a branching factor larger than 3. The landmarks used are labeled with the step number depicted in Figure 7. The arrows indicate the intended walking direction.

tively perceived helpfulness of the chosen salient objects could additionally be examined. To illustrate which interface element is referenced by each question, the last form also contained a labeled depiction of an exemplary screen. 5.4. Results

To validate the research hypothesis that participants perform better with the graph-like interface, at rst the overall navigation time for each test person was computed. This is a popular procedure of similar studies (see e.g. [9]). All in all, 35 test users were assigned to the map UI, whereas 29 persons were guided with the graph interface. Unfortunately, the central normality assumption had to be rejected for both groups using the Shapiro-Wilk

test. Consequently, Wilcoxon's rank sum test was consulted. The results yielded that H0 can be rejected (p = 0.034). The participants reached their destination signicantly faster with the graph interface (mean time: 6 minutes 24 seconds) compared to the map interface (mean time: 6 minutes 42 seconds). The calculated eect size r for this test is 0.27, which is small to medium for empirical data. The corresponding boxplot is depicted in Figure 9. It was also noted, whether the test persons lost their way during the navigation task. With each prototype, 16 test persons needed help to reach their destination. This apparently shows that, concerning task success, no dierences between the system prototypes can be assumed. For a more detailed examination, two subsamples were gener-


17

Fig. 9. Boxplot of the overall navigation time in minutes for each system prototype.

ated: the rst of them only contained participants, who did not need help ("no-help"-subsample) and the other one included test users, who dependent on assistance ("help"-subsample). As expected, none of the subgroups was normally distributed, so that once again the Wilcoxon's rank sum test was used with the overall task completion time as the main dependent variable. For the "help"sample, no signicant dierence for the two interfaces could be determined (p = 0.160). However, for the remaining subsample of participants who did not need help, the dierence is significant with a medium eect size (p = 0.0302, r = 0.38). It seems that participants get lost independently from their respective prototype. Therefore, the "help"-subsample was closer examined. It turned out that more than half of the users lost orientation at landmark 17 depicted in Figure 7 and 8. The corresponding screenshots of this navigation step can be seen in Figure 10 and 11. At this point, the test persons had to cross the hall of the central library building. The instruction says to go left in front of the stairs. The test persons misinterpreted this instruction, since they thought they had to go left in a room (which most of the participants of the "help"-subsample then entered). However, in fact they were expected to go past the stairs without entering a room and click on the "Next"-button to see the following instruction. Moreover, when the users did not "zoom out", the referenced landmark icon, i.e. the stairs, was not visible in the interface area depicting the user's surroundings. Consequently, indoor areas like halls with a very high branching factor are environments where in-

Fig. 10. Screenshot of the scene where most participants lost their way ("Map"-Interface).

structions have to be more elaborate in some cases. For instance, at this specic point, an instruction that says "Go through the hall! " would support the users better, according to the observation of this study. An additional implication of this nding is that a pedestrian navigation system has to adapt the zoom factor so that all referenced landmarks are visible at any time. Nevertheless, at other points of the route with a branching factor of 5, participants performed well, if no change of direction was needed. This result is denitely a topic of further research. For example, a new concept in the mapping toolkit was developed that allows for the indication of areas. Instructions that derive from this concept have to be evaluated in further studies. 5.4.1. Analysis for each of the navigation steps

To gain deeper insights in the dierences of the performance of the test users, each of the 23 navigation steps was examined on its own. For this purpose, only the "no-help"-subsample described in the previous section was taken into account. This resulted in a test sample of 19 map users, 13 participants navigating with the graph interface. The time it took the participants to accomplish each navigation step was captured resulting in a set of 736 data points. Once again, using Wilcoxon's rank sum test, signicant dierences could be de-

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Fig. 13. Boxplot of the navigation time in seconds divided by system prototype for step 15.

Fig. 11. Screenshot of the scene where most participants lost their way ("Graph"-Interface).

Fig. 14. Boxplot of the navigation time in seconds divided by system prototype for step 23 (nal step).

Fig. 12. Boxplot of the navigation time in seconds divided by system prototype for all steps.

tected for the two UI groups (p = 0.043). If participants used the graph interface (mean = 15.3 seconds) they accomplished the single steps faster evaluated compared to the map prototype (mean = 17.3 seconds). This result is presented in Figure 12. However, it has to be noted that the calculated eect size is really small (r = 0.08). Subsequently, every step on its own was tested for normality using the Shapiro-Wilk test. For the steps where the normality assumption has not been rejected, a subsequent test for variance homogeneity was performed with the Levene test. Eight steps were nor-

mally distributed and variance homogeneity was ensured. For this steps a t-test was performed. The remaining data sets were tested for dierences with Wilcoxon's rank sum test. Signicant results were found for step 15 (p = 0.011) and 23 (p = 0.036) of the route depicted in Figure 7, for which a t-test could be performed. Figure 8 and 16 give an impression of the particular navigation scenes. The corresponding boxplots are shown in Figure 13 and 14. Both steps could be accomplished relatively fast with the graph interface (mean = 11.8 and 10.6 seconds). Cohen's d showed large eect sizes for both steps (step 15 = 0.969 ; step 23 = 0.734). Taking a closer look at step 15, it is a rather easy instruction to simply go up the stairs (see Figure 15 for the graph interface). This is very similar to the last step: this instructions says "Your destination is on the left". Even though these deci-


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Table 5 Number of participants assigned to previous knowledge of place Knowledge of place Total number of participants using the graph interface using the map interface

Fig. 15. Screenshot of the interface presented at step 15.

sion points are rather complex in terms of branching factor (see Figure 7), the depicted landmarks seemed to be highly salient and therefore, participants could recognize them very fast. This is in line with the research presented in the related work section. Some people experience maps as cognitively demanding or even distracting. In conclusion, the graph interface seems to reduce the eect of unintentional blindness described in [37]. 5.4.2. Results of the post-experiment questionnaire

The participants were asked to ll in a form after the experiment that related to their subjective experience with their respective navigation prototype. For the sake of clarity these items are once again listed at this point: 1. I was very familiar with the test route before the experiment. 2. There were enough salient objects displayed, so that I could orient myself. 3. The chosen salient objects (like doors or stairs) were helpful. 4. The directional arrows helped me while navigating. 5. The text instruction helped me while navigating. 6. The depiction of the surroundings helped me while navigating. 7. The automatic alignment of the map helped me while navigating. 8. I felt comfortable while navigating.

"bad" 30 11 19

"good" 34 18 16

The results of all participants are taken into account for the following tests (not only the "nohelp" subsample described in Section 5.4). For each question the assumption for normality had to be rejected using the Shapiro-Wilk test. Subsequently, every question was analyzed whether the participants indicated subjectively experienced dierences concerning the reception of their respective interface, i.e. map or graph. The results of Wilcoxon's rank sum test show no signicant differences. Even though participants reach their destination faster with the graph-like system, they do not dier concerning their experience while navigating. Table 4 shows an overview of the questionnaire results. Possible impacts implied by question 1 are discussed in detail in the next section and are therefore not reported in table 4. All in all, the values are rather high. Concerning question 2 and 3, which were asked to examine how the chosen landmarks and their depictions were perceived, participants found the objects very helpful (6 out of 7). Overall, test persons stated that there were enough salient objects displayed (5.5 out of 7). However, it is topic of further research if the depiction of more objects could help during the waynding process. Moreover, all participants felt rather comfortable while navigating, even though this analysis included participants who got lost at some navigation steps. 5.4.3. Dependence on knowledge of the place

The post-experiment questionnaire contained a question, where participants had to indicate whether they were familiar to the test route before the experiment on a 7-point Likert scale. Considering this variable, the test group was rather heterogeneous: on average this question was answered with 3.8 of possible 7, with a standard deviation of 2.2. Consequently, it was examined whether this variable had an inuence on the overall task completion time. For this purpose the test group was divided in two parts: on the one hand, into par-

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Fig. 16. Screenshot of the interface presented at step 23 (nal step) and photograph of the scene. Table 4 Answers to the post-experiment questionnaire (on a 7-point Likert scale with 1: strongly disagree to 7: strongly agree). Question Answer (mean)

2 5.5

3 6.0

ticipants having a "bad" knowledge of place (values 1-3), which results in a subgroup of 30 persons. On the other hand, 34 test persons with a "good" knowledge were identied (values 4-7). Table 5 shows the detailed distribution of this variable. The normal distribution assumption had to be refused for all groups using the Shapiro-Wilk test taking into account the whole navigation time as the dependent variable. Accordingly, Wilcoxon's rank sum test was conducted, which showed that no performance dierence could be detected, neither concerning participants with a good previous knowledge of space (p = 0.134), nor a bad (p = 0.126). Thus, it does not depend on previous knowledge of the place, whether participants prefer the map or the graph interface. Both systems, and in particular the graph presentation evaluated in this paper, support both user groups. In addition, it was examined whether participants who were familiar to the test route performed better than those who had no previous knowledge of place independently of the interface they were using. Wilcoxon's rank sum test revealed no signicant dierences concerning this factor (p = 0.607).

4 5.4

5 6.0

6 5.0

7 5.0

8 5.8

5.4.4. Dependence on sense of direction

Recently, Bienk et al. 2013 [2] argued that the user's performance in a navigation-like subtask highly depends on the Sense of Direction (SoD). For achieving comparable results, the same variable was recorded in the main experiment using a German language self-report scale [30]. Answers had to be given on a 7-point Likert scale. At rst it was examined whether well-oriented participants reach their destination faster without discriminating the dierent interface types "map" and "graph". Since the data was not normally distributed, which was analyzed with the ShapiroWilk test, a Wilcoxon rank sum test was conducted and showed no signicant dierences (p = 0.250). Extending the idea that participants with a well-developed sense of direction prot from interfaces other than maps, two additional research hypotheses were devised. H2 : Participants with a high SoD perform better with the graph interface. H3 : Participants with a low SoD perform better with the map interface. Consequently, the subsamples of participants with a "high" (values 4-7) and "low" (values 1-3) SoD were generated using the rounded mean of all 19 questionnaire items (see table 6). For all groups


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Table 6 Number of participants with assignment to sense of direction. Sense of direction Total number of participants using the graph interface using the map interface

"low" 37 17 20

"high" 27 12 15

the normality assumption had to be rejected with the overall navigation time as the dependent variable using the Shapiro-Wilk test. Wilcoxon's rank sum test revealed that the SoD has no signicant inuence on performance for users with a low SoD, so that no evidence can be provided for H3 . However, the subsample of welloriented users revealed signicant dierences (p = 0.02 , r = 0.446). Consequently, H2 can be assumed for these user groups. It turned out that especially users with a high SoD reach their destination faster with the graph-like UI (mean = 6.34 minutes) compared to the map interface (mean = 7.06 minutes). Consequently, the same procedure as described in Section 5.4.1 was employed: each step for the subsample of well-oriented persons was analyzed on its own, using only participants, who reached the destination without help. As expected, similar ndings are gained: especially for step 15, well-oriented participants are signicantly faster with the graph-like UI (p = 0.017, Cohen's d = 1.272, mean time graph = 12.0 seconds, mean time map = 18.3 seconds, see Figure 17). The results for step 23 could not be reproduced due to several missing values and consequently very small group sizes. Considering the subjective experience participants had with their respective navigation prototype evaluated using the post-experiment questionnaire no signicant dierences could be found for the subgroups of users with a "high" or "low" sense of direction. 5.4.5. Discussion

All in all, the results reported in the previous sections show that participants perform better with the graph-like interface compared to the mobile map depiction in terms of task completion time. Moreover, the ndings of the post-experiment questionnaire, dealing with the subjective experience test persons had, prove that both interfaces perform well in terms of usability and user satisfaction. However, since H2 could be conrmed, the

Fig. 17. Boxplot of the navigation time in seconds divided by system prototype for step 15 and well-oriented users.

general ndings reported in Section 5.4 are clearly degraded. Only for participants with a well developed sense of direction, the graph interface can be highly recommended. Apparently, these persons prot most from the very simple and abstract interface type and unintentional blindness can be prevented for this user group, especially if the current navigation scene contains very salient landmarks. Yet, bad-oriented users have no signicant interface preference: therefore, it is advocated that the graph-like UI can still be used for all user groups, if no information about user characteristics is available. Of course, since the group sizes in the main study sometimes were quite small due to the generation of "subsubgroups", further studies with more participants are required to conrm these ndings, especially concerning the fact that many tests were conducted. It has to be noted that the main variable "navigation time" also depends on walking speed, which could not be controlled in this experiment. However, no participant had any (obvious) walking disabilities. In addition, each navigation step, at which the test persons got lost, has to be qualitatively examined in further detail. One problem - Stahl and Haupert [46] pointed out - is that users do not follow the angular course of modeled edges, especially while navigating through large outdoor areas or hallways. The experienced problem that users lost orientation in the hallway at landmark 17, reects this weakness. As stated above, the modeling toolkit was supplemented with an additional "area node" that can be used to ag such large areas to counteract this problem. This will be a topic of further research.

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Taking into account that half of the participants made a wrong turn during the experiment clearly shows that the navigation prototypes have to be improved to support waynding in real-world scenarios. The improved system could e.g. depict more than one landmark at each point to support self-localization during the navigation task. Moreover, dierent approaches to handle a "lost" situation have to be implemented and evaluated. Even with a very good interface it is possible that pedestrians loose their way while navigating. In this case, a new route could be calculated if the user walks in the wrong direction given that some kind of localization technique is available. Another possible solution to support orientation during a "lost" situation could be to choose dierent landmarks in the users' surrounding to explain the route since the current landmark may not be visible due to some temporal obstacles. Generally, the most important issue is to examine how the map design can be improved in order to nd a compromise between the very reduced and abstract graph interface and the map material currently used to navigate the users. For instance, all text labels in the map could be excluded and only shown if the room is referenced in the instruction. This information could also be included if several similar looking landmarks are located next to each other and have an unambiguous label like "lecture hall 11" to clarify the situation. Furthermore, the current route part could be highlighted in order to clearly indicate which part of the current route the user is located at. Another limitation of the main study and therefore a topic of further research is the automatic generation of text instructions. These were formalized by the test supervisor for the main study and followed a xed "landmark" + "direction" pattern. Results showed that especially if the current decision point is quite complex this simplied instruction is not enough to navigate. Regarding the formalization of "optimal" route instructions several studies have been conducted. For instance, [26] showed that instructions taking into account the whole navigation scene do support waynding better compared to step-by-step guidance. Nevertheless, it is an open question how this text instructions can be generated automatically and if they have to dier according to the complexity of the route segment.

Finally, the main study was conducted with a tablet that had a large screen compared to a smart phone. As a result, especially the graph interface showed a lot of white space and therefore the available screen area was not optimally used. In future studies evaluations on smart phones have to be conducted in order to examine the inuence of this variable on task completion time. 6. Conclusion and future work

In this paper, an approach for developing indoor pedestrian navigation systems comprising dierent aspects, from the development of an environment model, selection of suitable landmarks to depicting routes and salient objects to the user is presented. With the introduced map modeling toolkit a various range of indoor areas like shopping malls, train stations, and university buildings can be created fast and easily, whereas still enough information is included to calculate routes that are adapted to the user's preferences. In the future, studies concerning the automatic generation of routing instructions enriched with landmarks have to be conducted in order to ensure an optimal user experience and performance. In particular, the new approach to model open space areas and give route instructions at such complex decision points has to be evaluated. Moreover, the pre-studies on landmark selection in dierent indoor environments, i.e. a university building, a shopping mall and a train station showed that "functional" salient objects like stairs and doors are suitable for navigation. Fortunately, these architectural elements are in any case part of out environment model. Therefore, only small adjustments had to be made to include theses objects in navigation instructions derived from the data model. However, especially taking into account that the test route in the train station was very short, the ndings of the prestudies have to be validated in other indoor areas to ensure they are applicable for a broader range of buildings. The main study was conducted to answer the question how navigation instructions should be displayed, provided that routes and landmarks are available. For this purpose an abstract graph-like interface directly derived from the environment models was evaluated against an interface addi-


tionally showing a map representation. This was motivated by an analysis of related work that indicated that map presentations are a good navigation aid, but still can cause additional cognitive load. The study revealed that participants with a high Sense of Direction perform signicantly better with the graph-like UI in terms of task completion time, especially if the current navigation scene provides highly salient landmarks. The participants performed equally good, no matter how familiar they were to the test route before the experiment. Moreover, the systems were rated relatively high in terms of subjective user experience. For this variable no dierences concerning the interfaces could be detected. All in all, the main study shows that not necessarily detailed information about the user's surroundings is needed to guide a pedestrian. Therefore, it is advocated that the data derived from the map modeling toolkit is sucient to implement an indoor navigation system that supports waynding. In the future, similar navigation experiments in other indoor areas have to be conducted in order to ensure the results of the pre-studies and main study at once. Furthermore, a more detailed survey concerning the experienced cognitive load during navigation with the graph-like interface can provide additional information if this UI does not only decrease task completion time, but also the cognitive eort it takes to accomplish the task. In this context, other user groups like elderly participants or children could also be interviewed. Results of the post-experiment questionnaire suggest that participants would prefer to see more landmarks on the screen. Therefore, a follow-up study on the depiction of landmarks is currently conducted mainly addressing the question how many objects should be displayed. It has to be examined, which salient objects have to be referenced in the textual navigation instruction and in the icons showing arrows enriched with landmarks. Moreover, dierent designs of this interface elements have to be evaluated in the future. Acknowledgments

The project described in this paper is funded by the German Federal Ministry of Economics and Technology (BMWi) under grant number 19 P 12009 F.

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References [1] C. Anagnostopoulos, V. Tsetsos, P. Kikiras, OntoNav: A semantic indoor navigation system, in: 1st Workshop on Semantics in Mobile Environments (SME05), Ayia, 2005. [2] S. Bienk, M. Kattenbeck, B. Ludwig, M. Müller, C. Ohm, I want to view it my way: interfaces to mobile maps should adapt to the user's orientation skills, in: Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, New York, ACM, 2013. [3] S. Bigler, A. Brügger, F. Utzinger, K.F. Richter, Up, Down, Turn Around: Assisted Waynding Involving Level Changes, in: Spatial Cognition IX, 2014, Springer, pp. 176189. [4] A. Bouwer, F. Nack, A. El Ali, Lost in navigation: evaluating a mobile map app for a fair, in: Proceedings of the 14th ACM international conference on Multimodal interaction, ACM, 2012, pp. 173180. [5] C. Brenner, B. Elias, Extracting landmarks for car navigation systems using existing GIS databases and laser scanning, International archives of photogrammetry remote sensing and spatial information sciences 34.3/W8, 2003, pp. 131138. [6] B. Brunner-Friedrich and V. Radoczky, Active landmarks in indoor environments, in: Visual Information and Information Systems, Springer, Berlin Heidelberg, 2006, pp. 203215. [7] A. Butz, J. Baus, A. Krüger, M. Lohse, A hybrid indoor navigation system, in: Proceedings of the 6th International Conference on Intelligent User Interfaces (IUI), ACM, New York, 2001, pp. 2532. [8] D. Cadu, S. Timpf, On the assessment of landmark salience for human navigation, in: Cognitive processing, 2008, pp. 249267. [9] L. Chittaro, S. Burigat, Augmenting audio messages with visual directions in mobile guides: an evaluation of three approaches, in: Proceedings of the 7th international conference on Human computer interaction with mobile devices and services, MobileHCI '05, ACM, New York, 2005, pp. 107114. [10] H. Cuayahuitl, N. Dethlefs, Spatially-aware dialogue control using hierarchical reinforcement learning, ACM Transactions on Speech and Language Processing (TSLP) 7.3, 2011, pp. 5. [11] M. Duckham, S. Winter, M. Robinson Including landmarks in routing instructions. Journal of Location Based Services, 4(1), 2010, pp. 2852. [12] P. M. Dudas, M. Ghafourian, H. Karimi ONALIN: Ontology and algorithm for indoor routing, Tenth International Conference on Mobile Data Management: Systems, Services and Middleware - MDM09, IEEE, 2009, pp. 720725. [13] S. Fontaine, Spatial cognition and the processing of verticality in underground environments, Spatial Information Theory, Springer, Berlin Heidelberg, 2001, pp. 387399. [14] GML, OGC, Geography Markup Language (GML) 3.0 Implementation Specication, OpenGIS Consortium, 2003.

24


[15] M. Goetz, A. Zipf, Extending OpenStreetMap to indoor environments: bringing volunteered geographic information to the next level, CRC Press, Delft, The Netherlands, 2011. [16] C. Hölscher, T. Meilinger,G. Vrachliotis, M. Brösamle, M. Knau, Up the down staircase: Waynding strategies in multi-level buildings, Journal of Environmental Psychology 26.4, 2006, pp. 284299. [17] H. Kim, G. Reitmayr, W. Woo, IMAF: in situ indoor modeling and annotation framework on mobile phones, Personal and ubiquitous computing 17.3, 2013, pp. 571 582. [18] A. Klippel, C. Freksa, S. Winter, You-are-here maps in emergencies - the danger of getting lost. Journal of spatial science, 51(1), 2006, pp. 117131. [19] M. Kluge, H. Asche, Validating a Smartphone-Based Pedestrian Navigation System Prototype, in: B. Murgante, O. Gervasi, S. Misra, N. Nedjah, A. Rocha, D. Taniar, B. Apduhan (eds.), Computational Science and Its Applications - ICCSA 2012, Vol. 7334 of Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 2012, pp. 386396. [20] C. Kray, C. Elting, K. Laakso, V. Coors, Presenting route instructions on mobile devices, in: Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI), ACM, New York, 2003, pp. 117124. [21] M. Levine, You-are-here maps psychological considerations, Environment and Behavior 14.2, 1982, pp. 221 237. [22] K. J. Li, J. Y. Lee, Basic Concepts of Indoor Spatial Information Candidate Standard IndoorGML and its Applications, in: Journal of Korea Spatial Information Society 21(3), 2013, pp. 110. [23] J. Link, F. Gerdsmeier, P. Smith, K. Wehrle, Indoor navigation on wheels (and on foot) using smartphones, International Conference on Indoor Positioning and Indoor Navigation, Vol. 13, 2012, p. 15. [24] B. Lorenz, H. J. Ohlbach, E.-P. Stoel, A hybrid spatial model for representing indoor environments, in: Web and Wireless Geographical Information Systems, Springer, 2006, pp. 102112. [25] B. Ludwig, M. Müller, C. Ohm Empirical Evidence for Context-aware Interfaces to Pedestrian Navigation Systems, KI-Künstliche Intelligenz 28.4, 2014, pp. 271 281. [26] V. Mast, C. Jian, D. Zhekova, Elaborate descriptive information in indoor route instructions, in: Proceedings of the 34th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, 2012. [27] A.J. May, T. Ross, S.H. Bayer, M.J Tarkiainen, Pedestrian navigation aids: information requirements and design implications, Personal and Ubiquitous Computing 7(6), 2003, pp. 331338. [28] J. Miller, L. Carlson, Selecting landmarks in novel environments, Psychonomic Bulletin and Review 18, 2011, pp. 184191. [29] M. Müller, C. Ohm, B. Ludwig, Path Network Modeling of Public Buildings, IOSpaces, GIScience, Vienna, 2014. [30] S. Münzer, C. Hölscher, Development and validation of a self-report measure of environmental spatial strate-

gies, Diagnostica 57 (3), 2011, pp. 111125. [31] S. Münzer, H. D. Zimmer, M. Schwalm, J. Baus, I. Aslan, Computerassisted navigation and the acquisition of route and survey knowledge, Journal of Environmental Psychology 26 (4), 2006, pp. 300308. [32] Versatile Route Descriptions for Pedestrian Guidance in BuildingsConceptual Model and Systematic Method, in: Proceedings of the 11th AGILE International Conference on Geographic Information Science, 2008. [33] http://www.openstreetmap.de, 2015. [34] http://wiki.openstreetmap.org/wiki/Indoor_Mapping, 2015. [35] T. Partala, A. Nurminen, T. Vainio, J. Laaksonen, M. Laine, J. Väänänen, Salience of visual cues in 3D city maps, in: Proceedings of the 24th BCS Interaction Specialist Group Conference, BCS '10, British Computer Society, Swinton, UK, 2010, pp. 428432. [36] T. Partala, M. Salminen (2012). User experience of photorealistic urban pedestrian navigation, in: Proceedings of the International Working Conference on Advanced Visual Interfaces, ACM, New York, 2012, pp. 204207. [37] A. Puikkonen, A.-H. Sarjanoja, M. Haveri, J. Huhtala, J. Häkkilä, Towards designing better maps for indoor navigation: experiences from a case study, in: Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia, ACM, New York, 2009, p. 16. [38] V. Radoczky, How to design a pedestrian navigation system for indoor and outdoor environments. in: Location based services and telecartography, Springer, Berlin Heidelberg, 2007, pp. 301316. [39] M. Raubal, S. Winter, Enriching Waynding Instructions with Local Landmarks, in: M. Egenhofer, D. Mark (eds.), Geographic Information Science, Vol. 2478 of Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 2002, pp. 243259. [40] K. Rehrl, E. Haeusler, R. Steinmann, S. Leitinger, D. Bell, M. Weber, Pedestrian Navigation with Augmented Reality, Voice and Digital Map: Results from a Field Study assessing Performance and User Experience, in: G. Gartner, F. Ortag (eds.), Advances in LocationBased Services, Lecture Notes in Geoinformation and Cartography, Springer, Berlin Heidelberg, 2012, pp. 3 20. [41] P. Ruppel, C. Klein, C. Linnho-Popien, Indooria-A Platform for Proactive Indoor Location-Based Services, in: Proceedings of the IEEE Global Communications Conference Exhibition & Industry Forum (Globecom 2008), Vol. 30, New Orleans, LA, USA, 2008. [42] F. Röser, A. Krumnack, K. Hamburger, M. Knau A four factor model of landmark salience - A new approach, Proceedings of the 11th International Conference on Cognitive Modeling (ICCM), 2012, pp. 8287. [43] R. Sefelin, M. Bechinie, R. Müller, V. Seibert-Giller, P. Messner, M. Tscheligi, Landmarks: yes; but which? ve methods to select optimal landmarks for a landmarkand speech-based guiding system, in: M. Tscheligi, R. Bernhaupt, K. Mihalic (eds.), MobileHCI 05. Proceedings of the 7th International Conference on Human Computer Interaction with Mobile Devices and Ser-

C. Ohm et al. / Displaying landmarks and the user's surroundings in indoor pedestrian navigation systems vices, ACM, New York, 2005, pp. 287290. [44] N. J. J. M. Smets, G. M. te Brake, M. A. Neerincx, J. Lindenberg, Eects of mobile map orientation and tactile feedback on navigation speed and situation awareness, in: Proceedings of the 10th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI '08, ACM, New York, 2008, pp. 7380. [45] M.E. Sorrows, S.C. Hirtle, The nature of landmarks for real and electronic spaces, in: Spatial information theory. Cognitive and computational foundations of geographic information science, Springer, 1999, pp. 3750. [46] C. Stahl, J. Haupert, Taking location modelling to new levels: A map modelling toolkit for intelligent environments, Location-and Context-Awareness, Springer, Berlin Heidelberg, 2006, pp. 7485. [47] C. Stahl, T. Schwartz, Modeling and simulating assistive environments in 3-D with the YAMAMOTO toolkit, International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2010, pp. 16. [48] A. Stark, M. Riebeck, J. Kawalek, How to Design an Advanced Pedestrian Navigation System: Field Trial Results, in: 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007, pp. 690694. [49] F. Taher, K. Cheverst, Exploring user preferences for indoor navigation support through a combination of mobile and xed displays, in: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, ACM, New York,

25

2011, pp. 201210. [50] B. Walther-Franks, R. Malaka, Evaluation of an augmented photograph-based pedestrian navigation system., in: A. Butz, M. Christie, B. Fisher, A. Krüger, P. Olivier (eds.), Smart graphics, 9th International Symposium, Springer. Berlin Heidelberg, 2008, pp. 94105 [51] S. Winter, Route Adaptive Selection of Salient Features, in: W. Kuhn, M. Worboys, S. Timpf (eds.), Spatial Information Theory, Foundations of Geographic Information Science, Vol. 2825 of Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 2003, pp. 349361. [52] S. Winter, M. Raubal, C. Nothegger, Focalizing Measures of Salience for Waynding, in: L. Meng, A. Zipf, T. Reichenbacher (eds.), Map-based Mobile Services: Theories, Methods, and Design Implementations, Springer Geosciences, 2004, pp. 127142. [53] M. Worboys, Modeling indoor space, in: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, ACM, 2011, pp 16. [54] L. Yang, M. Worboys, A navigation ontology for outdoor-indoor space:(work-in-progress), in: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, ACM, 2011, pp. 31 34. [55] X. Yin, P. Wonka, A. Razdan, Generating 3d building models from architectural drawings: A survey, IEEE Computer Graphics and Applications, 29(1), 2009, pp. 2030.

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