Wayfinding in Virtual Environments Using an Interactive Spatial Cognitive Map Rameshsharma Ramloll & Darren Mowat Computing Science Department, University of Glasgow, G12 8QQ {ramesh,
[email protected]}
Abstract We illustrate how an interactive spatial cognitive map representation of a large-scale virtual space under navigation can be constructed in order to help solving navigation queries and formulate navigation commands. We explain our design, describe its implementation and present the evaluation results from a sample of 6 navigators. Our balanced-within experiment shows that our implementation results in a significant decrease in the physical demand experienced by participants to tackle set navigation tasks. This paper also introduces a novel algorithm for the capture and organisation of spatial knowledge resulting from navigation in a virtual environment. This algorithm is the basis of our navigation aid.
Keywords Large-scale virtual worlds, cognitive map, wayfinding, direct manipulation, subjective workload
1. Introduction Large-scale virtual environments [13] include detailed simulations of complex architectural constructs such as oilrigs and multi-storey buildings; others are more abstract in nature. Examples of the latter include spatialised databases such as populated information terrains [1][8]. Designers propose tackling wayfinding in these environments using strategies based on studies of navigation in the real world [14]. It has been argued that making virtual environments legible is a promising solution to wayfinding problems [3][4] experienced in such spaces. This has led to attempts to automate the process of making abstract large-scale virtual worlds legible [9]. A more recent attempt to facilitate wayfinding is based on the concept of worldlets [6]. These are 3D representations of virtual environment
landmarks that can be interactively viewed. They have been proposed to deal with the lack of depth cues and context needed to reliably recognise landmarks. In our opinion, the strength of this approach lies mainly in the fact that the user can have access to landmarks from multiple vantage points thereby increasing their likelihood to be recognised, and thus to become useful. We address wayfinding problems in large-scale virtual environments with an approach based on interactive spatial cognitive maps to the wayfinding problems in large-scale virtual environments. A spatial cognitive map [5] has been defined as the body of knowledge of a large-scale environment constructed by integrating observations gathered over time, in order to find routes and determine the relative position of places [11]. The cognitive map of space is held in the mind, a physically unobservable structure of information that represents spatial knowledge [10]. This is distinct from the geometric model of space which results mainly from direct measurements, is public and directly accessible for mathematical manipulations. Our strategy relies on (1) making this spatial cognitive map as public as possible and (2) enabling its direct manipulation to facilitate wayfinding. The strategy we propose is centred on the concept of defining place as a sensory snapshot. A place is “wherever you are when you experience a given sensory image” [12] rather than simply a set of Cartesian coordinates. We wish to stress at this early stage that we are making no claims about the cognitive processes that really happen in the mind when spatial information is captured or stored. Rather, what we are proposing is an aid, intended to be situated between real world navigation processes, and all those hidden processes inside the mind. Typically, a large-scale 3D virtual environment, such as a hyperlinked VRML world, is explored using a 3D browser such as CosmoPlayerTM (Figure 1, arrow 1). During this exploratory phase, the navigator constructs a
private representation of the place ‘in the mind’. This is the so-called spatial cognitive map of the place (Figure 1, arrow 2). Our aim is to capture, store and represent the latter. This is achieved by capturing sensory snapshots and relevant navigational inter-relationships in order to create a hypermedia representation of the cognitive map, which we here after refer to as a cognitive web of the navigated space (Figure 1, arrow 3). The latter is not useful if the information it contains is not easily accessible. A cognitive web browser is needed to access such spatial information about a 3D virtual environment (Figure 1, arrow 4). The browser fulfils the following functions: the maintenance and repair of the cognitive map (Figure 1, arrow 5) and the formulation of navigation commands for autonomous navigation (Figure 1, arrow 6).
inter-relationships between them. We show shortly the importance of this abstract construct in describing the organisation of sensory snapshots into the cognitive web. Two main constructs of SSDs are space-nodes and transit boundaries. (i) Space-node Space may be subdivided into functionally distinct regions, each of which is referred to as a space-node. Space-nodes, rather than focusing on part/subpart relationships between spatial regions, emphasise more the transitory relationships between them. The strategy for dividing a large-scale environment into space-nodes depends on the designers of the virtual environments. For example in our case, the size of the file, the complexity of the environment to be represented and the meaningful organisation of 3D objects in the virtual space determined the choice of space-nodes. (ii) Transit-boundary This is the interface between two space-nodes. For example, a door may be regarded as being a transit boundary between two spaces ascribed to different functions, such as two different rooms or a room and a corridor. A transit boundary is constrained to be unidirectional. Thus, a door, which allows bi-directional navigation between two space-nodes, according to this model, should be considered as two opposite transit boundaries. A transit boundary is represented by a directional edge with the additional property that it cannot loop back to the node it is leaving. In the example described in Figure 2, rooms A, B, C, and Outside are chosen to represent 4 functionally distinct spaces. Figure 3 shows its corresponding SSD.
Figure 1 Overall strategy to tackle wayfinding problems in large-scale virtual worlds
2.Creating a cognitive map representation A mechanism for capturing and organising sensory snapshots is developed to create the cognitive web. The latter can then be accessed and directly manipulated for information retrieval purposes and for the formulation of navigation commands or queries. Before describing the mechanism, it is necessary to define a few abstract constructs to facilitate the description of our approach.
Figure 2 Floor plan of a very simple building
2.1 Necessary abstractions for explaining our spatial information gathering strategy We propose Space Structure Diagrams (SSDs) to represent large-scale spaces using graphs. A Space Structure Diagram is a directed graph used to represent distinct spatial regions in 3D worlds and the navigational
Figure 3 SSD representation of the section described by the floor plan in Figure 1
We now construct a browsable representation of the cognitive map based using: (i) Entry transit boundaries: Transit boundaries that allow a space-node under consideration to be visited when crossed. (ii) Exit transit boundaries: Transit boundaries that allow a given space-node to be exited when crossed. 1.
(iii) Views: Special point of views that reveal salient or landmarked [sic] sensory data of interest to the navigator within a given space-node. In our current implementation, 4x4 cm2 pictures represent each of these entities which constitute the building blocks of the cognitive map representation.
2.2 Representing transit boundaries and Views using pictures Entries, Exits and Views are pictorial representations of entry transit boundaries, exit transit boundaries and views respectively. Each space-node S i has 3 sets associated with it, namely an Entry set N i , Exit set X i and View set Vi . Space-node, S i : is defined as a functionally distinct spatial region; Entry set, N i : contains sensory snapshots of entrances to space-node S i ; Exit set, X i : contains sensory snapshots of exits from space-node S i ; View set, Vi : contains salient sensory snapshots specific to a navigator’s interest within space-node S i , where i ∈ {0,1,2,...}. Pictures, eα , β ,λ , (Figure 4) define a transit boundary where α , β , λ ∈ {0,1,2,...} and α ≠ β (recall that no self-referencing transit boundary is allowed in our model). More specifically, the latter is a sensory snapshot of the transit boundary between Sα and S β from within Sα (Figure 4). Since there may
be more than one transit boundary between Sα and S β , we introduce an additional index variable λ to distinguish between them. While Views are created at will by the user, Entries and Exits are produced automatically when the navigator proceeds through a transit boundary from one spacenode to another. These pictures are collected in three sets according to the following rules. Say, a navigator is moving from a space-node, Sα , to another one, S β through the transit boundary eα , β ,λ meta-represented by (Figure 4).
2. 3.
X α ← X α ∩ {e α β λ } N β ← N β ∩ {eα β λ } V β ← V β ∩ {v β } , ,
, ,
,k
Figure 4 Space-node Sα linked to space-node
S β through a transit boundary eα , β ,λ On traversing the transit boundary of Sα , an Exit
eα , β ,λ is created and collected in its Exit set, X α (Figure 4 rule 1) and a copy of the most recently generated Exit is made and stored in the Entry set N β (Figure 4 rule 2). When carrying out explorations within a given space-node, the navigator can create Views,
vβ
,k
, where k is the index of the view captured in
space-node S β to illustrate his particular interests. These are then collected into the View set, V β , of the relevant space-node (Figure 4 rule 3). In Figure 4, the View was created in S β .
2.3 Example illustrating the capture and organisation of pictorial information about the virtual environment We use a simple navigation scenario to show how the contents of the relevant sets for each space-node evolve according to the rules described earlier. Figure 5, shows a navigator moving through four chosen spacenodes, Outside, room A, then B, and finally into C, through 3 transit boundaries, Door A, Door B and Door C. We examine the generation of Exits, Entries, and creation of Views to demonstrate our method of capturing and organising pictorial representations of the virtual environment. Note that in this scenario, the navigator moves through points P1 to P8, creating Views at P3 and P6, interacting with transit boundary at P4 etc... Please note that Entries and Exits are generated only during the first traversal of a transit boundary in a given direction.
2.4 Cognitive Web Construction (CWC) algorithm The CWC algorithm describes a simple procedure for capturing the spatial knowledge of a navigator during his exploration of a given 3D virtual environment represented as a SSD. Initial conditions Consider a navigator in a given starting space-node, S 0 .
N o = {arbitrary} where the arbitrary Entry Picture
illustrates the point of entry into the 3D virtual environment; X o = { }; Vo = { } . WHILE space-node S i is currently being navigated{ Figure 5 Navigating a very simple building
(1) IF View
At Point P1, in space-node, Outside (O) Entry set, N o = {arbitrary}An arbitrary picture is used to represent the point of entry into the 3D virtual environment. This represents the transition from the real world to the virtual world. Exit set, X o = eO , A, 0 ,
{
}
eO , A,0 is generated as result of the navigator’s interaction
with Door A (Figure 4 rule 1); View set, Vo = { } .
V A = { }.
{v }, v A, 0
A, 0
is
Point P4, in space-node, Room A N A = eO , A, 0 ; X A = e A,B , 0 from Figure 4 rule 1;
VA
{
}
A, 0
Point P5, in space-node, Room B N B = e A,B ,0 from Figure 4 rule 2; X B = { } ;
{
VB = { }.
i, k
is
added to the View set Vi , i.e.
Vi = Vi ∩
{v
i ,0
}
,..., vi ,k ,.. where
k ∈ {0,1,2,...} .
(2) IF a transit boundary is crossed for the first time to reach an adjacent space-node S j ,
X i = X i ∩ {ei , j ,λ }where λ ∈ {0,1,2,...}
AND a copy of this exit mark is inherited by the Entry set of space-node S j , N j , i.e.
N j = N j ∩ {ei , j ,λ }.
created by the user to indicate his interest in that particular part of the virtual environment.
{ } = {v }.
v
the Exit set of space-node S i , X i , i.e.
N A gets a copy of the most recently generated Picture in X o (Figure 4 rule 2) N A = {eO , A, 0 }; X A = { } ;
N A = {eO , A, 0 }; X A = { } ; V A =
is captured THEN
i, k
THEN an Exit ei , j ,λ is generated and added to
Point P2, in space-node, S A , Room A
Point P3, in space-node, Room A
v
}
Similar operations and spatial information capturing processes occur through P6, P7 and P8. We generalise the above scenario with The Cognitive Web Construction algorithm which describes our sensory snapshot accretionary mechanism more formally as follows.
(3) IF exploration is over STOP. }//While navigator is in current space-node S i
3. Interacting with the cognitive map representation A navigator can interact with the captured spatial information through a cognitive web browser in various modes. Recall that Views are captured during navigation within a 3D scene while Entries and Exits are created when moving through a transit boundary. In the ‘learn mode’ the navigator uses the browser to maintain, repair or query her own cognitive map. She may also rehearse navigation in a novel environment, if the cognitive web is borrowed from a peer. In the ‘command’ mode, the representation of the cognitive map is accessed in order to formulate navigation commands which are subsequently satisfied through autonomous navigation, with the navigator being transported to the destination specified. In practice, the navigator will have to shift frequently from one mode of interaction to another according to her navigational needs.
In the following, we describe an example cognitive web browser (Figure 6, arrow 4) to describe each mode of interaction. Its interface consists of 3 scrollable frames, the Views (Figure 6, arrow 1), the Exits (Figure 6, arrow 2), and the Entries (Figure 6, arrow 3) frames each containing members of the View, Exit and Entry sets respectively. At any one time, all the pictures belonging to only one given space-node are loaded into their respective frames. We now show how the navigator can interact with the interface of the cognitive web browser in various ways.
Figure 6 A cognitive web browser (5) next to a traditional 3D world browser (11)
3.1 Capturing and storing cognitive map Capturing and storing of the cognitive map occurs during the manual navigation of the 3D virtual environment using control buttons (Figure 6, arrows 6,7,8,9) of the 3D virtual environment Browser (Figure 6, arrow 11). During this operation, as explained earlier, the system captures the cognitive map of the navigator by generating appropriate Entries and Exits based on the interplay between the navigator and the transit boundaries. In addition, Views are also created as representations of locations landmarked by the navigator by clicking the View button (Figure 6, arrow 10). In this way, an evolving cognitive web describing the current status of her spatial knowledge is produced.
language which provides the navigator with information describing the current space-node, its constituent landmarked locations and possible entries and exits. Just as a reminder, Entries provide information on how I did get there and Exits on how can I leave from here. The cognitive web browser also allows the navigator to rehearse navigation by directly manipulating its interface. For instance, while her real position is static in the actual 3D environment, she can click an Exit to update the scrollable frames with the appropriate pictures of the space-node that the clicked Exit leads to. Alternatively, she may click an Entry to load the pictures of the space-nodes that lead to the current one. There is also provision for the user to undo or redo these operations as well by clicking the UNDO or REDO buttons. In short, the interface is designed in such a way that directly manipulating it allows the navigator to discover, question or check the interrelationships between pictures thereby allowing him to get an idea of the general topological structure of the virtual environment. We show how by interacting with the interface of the cognitive web browser alone, the navigator is allowed to visualise the corresponding navigation in the actual 3D virtual environment. Figure 7 shows the sequence of changes in the state of the interface as the navigator clicks on appropriate pictures. Clicking eAB (a) will cause the pictures of the space-node, Room B to be loaded. In turn, clicking eBC causes that of space-node, Room C, to be loaded (b). Note that this navigation operation can be reversed by clicking eBC (c) and eAB (d). Figure 8 describes the visualised navigation in the actual 3D virtual environment if steps (a) and (b) are followed. Another point to bear in mind in this example is that the View Vm has been created earlier during a tour of space-node B. Thus, interaction with the interface provides the navigator with a spatial language that gives her an insight into the explored space.
3.2 Repairing and querying spatial knowledge To repair or query the captured spatial knowledge, the navigator clicks the toggle button (Figure 6, arrow 5) to toggle into the learn mode. In this mode, the position of the navigator in the 3D environment remains fixed. The navigator can browse the captured cognitive web to reinforce, correct, or query her own cognitive map. The pictures in the scrollable frames of the Cognitive Web Browser together constitute a spatial descriptive
Figure 7 Direct manipulation of the cognitive web browser
Figure 8 Navigator moving from A to B, touring B, then moving to C
3.3 Transporting navigator to desired position and orientation In the command mode, accessed by clicking the toggle button, interactions with the pictures drive navigation in the 3D virtual environment. For example, clicking a picture causes the navigator to be transported automatically to the location where the ‘sensory snapshot’ represented by the picture can be experienced. Thus, if a navigator wishes to visit a particular location of interest in the 3D virtual environment, she can do so by finding the appropriate picture while in the learn mode. Once this is found, she can switch to command mode and click the picture to be autonomously navigated to the corresponding target location. In other words, switching to the command mode by toggling the appropriate button (Figure 6, arrow 4) informs the system that from that point onwards, clicking the pictures will cause the scene in the 3D virtual environment browser to change to reflect the automated navigation towards the location described by the clicked picture.
4. Implementation The Cognitive Web Browser and the 3D browser were implemented using the Java3DTM API and the NCSA Portfolio which is a collection of utility objects to use with Java 3D programs. The development platform was a Pentium III machine with 256 Mbytes of RAM running Windows98TM. The 3D scenes were created using 3ds max® and exported to a suitable format (.OBJ format) that the object loader we were using understands. This was much easier said than done because of the difficulties we experienced in using 3D objects that were not rendering correctly in our 3D browser. In addition our colourful 3D scenes produced in 3ds max® were rendered rather disappointingly in
monochrome within our 3D browser. This of course led the captured cognitive map to be represented using monochromatic images. To ensure that the capturing and storing of pictures did not inhibit navigation in the 3D world, a separate low priority thread was dedicated to this task. Users navigate within the 3D scene using the cursor keys, Up arrow key for forward motion, Down arrow key for backward motion, Left and Right keys for turning left and right. Hitting the Enter key creates a View, a thumbnail of the current scene displayed in the 3D world browser. Clicking ‘hot-spot’ objects e.g. doors in a scene causes (1) the creation of an Exit, another thumbnail of the current scene in the 3D world browser, for the Exit set of the current space node, (2) the creation of an Entry, a copy of the last thumbnail produced, for the Entry set of the space-node to be visited and (3) the loading of the new space-node in the 3D world browser. Figures 9 and 10 illustrate the look and feel of our application. It is to be noted that the implemented interface is not identical to the one described in the theoretical section of the paper. For example, the current interface does not provide the undo-redo functionalities for undoing and redoing manipulations of the cognitive web browser because of limited time available for the implementation and evaluation of the aid. We felt that these features were not critical for evaluating our approach to wayfinding problems. The reset view button simply settles the navigator at some appropriate position and orientation in a 3D scene. It functions as a panic button for users to press if they are totally lost and cannot orient themselves properly in the virtual space. The ‘show the scene’s snapshots’ button (see Figures 9 and 10) was enabled only in the learn mode and could be clicked to synchronise the state of the cognitive web browser with the space-node under navigation. For example, the user may have emerged in a space-node, and then wandered off for quite a while in the learn mode and wants to synchronise the state of the cognitive web with that of the 3D scene.
Figure 9 A view of the 3D browser (left) and cognitive web (right) where user, in the command mode, navigates within a space-node representing a city
Figure 10 A view of the 3D browser (left) and cognitive web (right) where user, in the command mode, navigates within a space-node representing a transport museum
5. Pilot study Participants: 6 participants took part in this pilot study (5 males and 1 female, all with no special training in navigating virtual 3D worlds, average age = 22.5 years). Experiment Design: Participants had to navigate a large-scale world consisting of 5 space-nodes. 3D objects (45 ranging from animals to transportation vehicles) obtained from public 3D repositories were distributed among the space-nodes. Before the administration of the tasks, participants were allowed 20 minutes to navigate the world, to familiarise themselves with the interface and to take snapshots whenever they wanted to. Participants were also allowed to extend their cognitive web by taking more snapshots during the tackling of the navigation tasks. After completing the latter, participants were administered the standard NASA TLX [7] to determine the workload they experienced. The time they took for completing every task was also captured. No time limit was imposed on participants for completing the tasks.
participants in the aided condition (T5 = 2.449, p = 0.029 < 0.05). Hypothesis H1 is thus confirmed. The mental demand experienced was higher in the aided condition than in the unaided condition. However, this effect was not significant (T5= -0.93, p = 0.196). H2 is neither confirmed nor disconfirmed. The overall workload experienced by participants in the aided condition was less than in the unaided condition. However, since this effect was not significant (T5 = 0.059, p = 0.477), H3 is neither confirmed nor disconfirmed. While the average time to locate an object in the first, second, third, and fifth tasks were less in the aided condition; this was not true for the fourth task. Also on the average, objects were located significantly faster in the third task (T5=2.46, p = 0.028 < 0.05). It appears that on the whole, locating the objects was faster with the aid than without the aid. But formally we cannot draw any conclusions because significance was not achieved either way.
Navigation Tasks: Each participant had to navigate the virtual environment in order to locate 5 objects under the aided condition and another 5 different objects in the unaided condition. 3 participants tackled the tasks in the unaided condition first and the other 3 tackled the tasks in the aided condition first. In the aided condition, they had access to the cognitive web browser. In the unaided condition, they could not use the cognitive web browser. This was done to minimise any effects resulting from fatigue and the learning of (1) the interface, (2) the environment. Note that, the order in which objects had to be located was random. Participants were also prevented from talking to each other. A participant was deemed to have located an object after the lowest tip of the object was made to meet the bottom edge of the 3D browser window.
20 18
Taskload index score
16 14 12 Aided
10
Unaided 8 6 4 2 0 M
Hypothesis H1: Participants experience less physical demand while tackling the tasks in the aided condition.
Ph
T
E
Pe
F
Workload categories error bars are shown)
W (standard
Hypothesis H2: Participants experience less mental demand while tackling the tasks in the aided condition. Hypothesis H3: Participants experience less overall workload while tackling the tasks in the aided condition. Hypothesis H4: Participants tackle the navigation tasks faster in the aided condition than in the unaided condition.
5.1 Results Our results show that there was a significant decrease in the physical demand experienced by
Figure 11 Comparing workload categories (M: Mental, Ph: Physical, T: Temporal, E: Effort, Pe: Performance, F: Frustration) and overall workload (W)
P T5
M
Ph
T
E
Pe
F
W
0.196
0.029
0.292
0.373
0.300
0.456
0.477
-0.93
2.449
0.584
0.343
0.560
0.117
0.059
Table 1 Paired two means t-test for workload categories and overall workload
80
Time (seconds)
70 60 50 Aided
40
Unaided
30 20 10 0 1
2
3
4
5
Order of localising target object (standard error bars are shown)
Figure 12 Comparing average time to complete tasks 1
2
3
4
5
P
0.392
0.164
0.028
0.352
0.290
T5
0.289
1.083
2.460
-0.402
0.589
Table 2 Paired two means t-test for time taken to complete tasks
We express some reservation about the strict validity of the statistical manipulations especially because of the small size of participant population.
5.2 User feedback and discussion of results We have collected here a few comments participants made while trying out the navigation aid. positive comments: “Using the navigation tools was noticeably faster”, “Snap shots were very ‘handy’ for quicker movement”, “Can position and reorient myself more easily and quickly”; “It helps me to remember more about the environment, I can remind myself of things in there without navigating through it again ” criticisms: “I was confused between the entries and exits when learning to use the system”, “Pictures describing some entries or exits are sometimes not easily distinguishable”, “System slows down while snapshot is being created…” We observed that some participants understood the interface of the cognitive map representation almost immediately. However, one participant in particular was lost on a number of occasions because he or she was not able to understand the difference between Exits and
Entries. The understanding of what Entries and Exits are has an important impact on the usability of the cognitive web. Although we implemented the system in such a way that while snapshots were being created on the fly, participants were still able to navigate normally, in practice this was unfortunately not the case. On a number of unpredictable occasions navigation would slow down or stop while pictures were being created or stored. This probably had to do with the varying complexities of different scenes and the idiosyncratic behaviours of Java thread implementation. We believe that this must have impacted negatively our evaluation certainly regarding the time taken by subjects to locate objects in the aided condition, the frustration and temporal demands experienced by participants. Also our implementation did not render the 3D scenes in colour. This resulted in the pictures captured to be less easily distinguishable from each other. We believe that an implementation which allowed a colour 3D scene to be navigated would also probably improve the results we obtained from our navigation aid. In short, this experiment may have had a bias against the strength of our approach. Another interesting observation is that even for this small pool of participants the strategy for creating snapshots varied widely. Some created Views based on their interests rather than on other concerns. This strategy is closer to what we had in mind but we were pleasantly surprised by participants whose strategies were more task-oriented. For example, some would also take snapshots from a distance to get as many objects as possible in the picture. This allowed them to identify very quickly the location of a target simply by flicking through the cognitive map representation rather than navigating manually through the world.
6. Future work Our pilot study shows that we have achieved a significant decrease in the physical demand experienced by users when solving the set tasks. This is a valuable achievement but we believe that this design can be improved to achieve significant gains in other areas, especially mental demand. For example, we can provide opportunities for users to annotate snapshots as they are created. These annotations can be made available in a way similar to tool-tips as the cursor is moved over the pictures. We anticipate this would help to disambiguate entries or exits that look similar. Currently, navigators are teleported from one position to another when users interact with the cognitive web browser in the command mode. It may be useful to look into ways to autonomously navigate [2] a user through the scene. Extensive teleportation in a
virtual environment can have a significant negative impact on the construction of the cognitive map of the spatial environment because of the unavailability of cues that will disrupt the relationship of one place with another [14]. In our opinion, it is not only the navigator constructing the cognitive web who will find it useful. Other prospective navigators could use the information that has been gathered about a given virtual world in various ways. The cognitive web of an experienced navigator could be shared with virtual tourists who just want to have a rough idea of how such and such worlds are before choosing one that suit them best. Cognitive webs could be used to determine the interest profile of a given navigator. In dynamic virtual environments, such as one shared by multiple avatars, the cognitive web can also function as a tool for recording events in that environment. So far we have captured only visual information which represents places. In principle, sounds heard at those places could also be captured. We hope that our approach will open new lines of enquiry in one area, which has unfortunately not received as much attention as it deserves, namely collaborative wayfinding in large-scale virtual worlds.
7. Conclusion This paper has presented an initial attempt to capture the cognitive map of a navigator in a virtual environment. The captured information is publicly accessible. We have illustrated how directly manipulating such cognitive webs allows navigators to tackle wayfinding problems in large-scale worlds. Our initial evaluation results indicate that interacting with such an approximate representation of the spatial knowledge is promising. It can certainly complement traditional techniques such as increasing the legibility of an environment to improve wayfinding. It may also be useful in cases where legibility approaches may fail e.g. the navigator may not understand the language the labels are written in. We also suggest that there is a lot of room for improving the current interface. Designers need to experiment with other ways of organising spatial information derived from a navigator’s knowledge of the environment in order to create an interactive publicly and intuitively accessible cognitive map of a navigator to tackle a wide range of wayfinding needs. We hope our work is a step in that direction.
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