Download - Evaluating Motion Constraints for 3D Wayfinding in Immersive and Desktop Virtual Environments
Evaluating Motion Constraints for 3D
Wayfinding in Immersive and Desktop Virtual
Environments
Niklas [email protected]
M. Eduard [email protected]
Philippas [email protected]
CHI 2008 – Florence, Italy
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Where am I? Where am I going?
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How to Aid Wayfinding?
• Wayfinding: navigation to solve specific task– Performed on cognitive map– Poor map leads to poor performance
• Problem: aiding wayfinding by supporting cognitive map building– Motion constraints and guides– Example: sightseeing tour of new city
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Cognitive Maps
Hotel
Tour Eiffel
Montmartre
Notre DameLouvre
Airport (CDG)
?
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Supporting Cognitive Maps
• GC: Global coverage– Expose viewer to whole environment
• CM: Continuous motion– Support spatial relations
• LC: Local control– Learning by doing
Hotel
Tour Eiffel
Montmartre
Notre DameLouvre
Airport (CDG)
?
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Virtual vs. Physical Worlds
• Why is wayfinding more difficult in virtual worlds?– Low visual fidelity– Mouse and keyboard poorly mapped to
3D navigation– Lack of sensorial cues
• High cognitive load on users
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Reducing Cognitive Load
• Method: Immersive Virtual Reality– Full 3D input– Full 3D output
• But: No widespread use, expensive (?)
• Mouse and keyboard are standard
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Guiding using 3D Motion Constraints
• Tour-based motion constraints (GC)• Smooth animation (CM)• Spring-based control (LC)
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User Study
• Hypotheses– H1: Guiding navigation helps wayfinding– H2: Local control improves familiarization– H3: Navigation guidance has higher impact for
desktop than for CAVE
• Controlled experiment (mixed design)• Two experiment sites• 35 participants
– 16 (4 female) on desktop computer– 19 (2 female) on CAVE system
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Factors
• Platform (BS): desktop or CAVE• Navigation (BS/WS): free, follow,
spring• Scenario (WS): outdoor, indoor,
infoscape, conetree• Collect distance, error, and time
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Procedure
• Phase I: Familiarization– Create cognitive map (5 minutes)– Supported by guidance technique– Three target object types
• Phase II: Recall– Locate two targets on overhead map
• Phase III: Evaluation– Collect third target in world – No navigation guidance
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Results – Summary
• Navigation method:– Free navigation: CAVE better– Motion constraints: desktop significantly
better (p < 0.05) [H1]
• Desktop platform:– Spring-based guidance gave better
accuracy than other methods [H2]– Navigation guidance more efficient than
free flight [H3]
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evaluation error
0
0,05
0,1
0,15
0,2
0,25
0,3
follow free spring
no
rma
lize
d e
rro
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CAVE
Desktop
average time per target
0
5
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15
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follow free spring
tim
e p
er
targ
et
(se
co
nd
s)
CAVE
Desktop
recall distance
0
0,05
0,1
0,15
0,2
0,25
0,3
follow free spring
no
rma
lize
d e
rro
r
CAVE
Desktop
Results – Details
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Conclusions
• Navigation guidance based on tours– Improve cognitive map building
• Evaluation on desktop and CAVE– Navigation guidance on desktop
outperforms CAVE– Less focus on interaction mechanics
• Take-away message: removing (some) freedom in 3D navigation may actually help wayfinding!
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Questions?
• Main findings:– Free-flight best on
immersive platforms– 3D guidance: desktop
outperform CAVE– Allowing local control
helped desktop
– Removing freedom improves performance
• Contact information– Niklas Elmqvist
– Edi Tudoreanu ([email protected])
– Philippas Tsigas ([email protected])
