Paper Info
Title
It's a long way to Monte Carlo: probabilistic display in GPS navigation
Abstract
We present a mobile, GPS-based multimodal navigation system, equipped with inertial control that allows users to explore and navigate through an augmented physical space, incorporating and displaying the uncertainty resulting from inaccurate sensing and unknown user intentions. The system propagates uncertainty appropriately via Monte Carlo sampling and predicts at a user-controllable time horizon. Control of the Monte Carlo exploration is entirely tilt-based. The system output is displayed both visually and in audio. Audio is rendered via granular synthesis to accurately display the probability of the user reaching targets in the space. We also demonstrate the use of uncertain prediction in a trajectory following task, where a section of music is modulated according to the changing predictions of user position with respect to the target trajectory. We show that appropriate display of the full distribution of potential future users positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data.
Year
DOI
Venue
2006
10.1145/1152215.1152234
Mobile HCI
Keywords
Field
DocType
system propagates uncertainty,gps navigation,augmented physical space,appropriate display,gps-based multimodal navigation system,system output,unknown user intention,inertial control,monte carlo sampling,monte carlo exploration,user position,probabilistic display,navigation,feedback,uncertainty,monte carlo,gps
Inertial frame of reference,Computer vision,Monte Carlo method,Time horizon,Computer science,Navigation system,Global Positioning System,Artificial intelligence,Probabilistic logic,Trajectory,Granular synthesis
Conference
ISBN
Citations 
PageRank 
1-59593-390-5
13
1.61
References 
Authors
7
3
Name
Order
Citations
PageRank
John Williamson1131.61
Steven Strachan219418.10
Roderick Murray-smith31396133.95