Did You See Bob?: Human Localization using Mobile Phones
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Transcript of Did You See Bob?: Human Localization using Mobile Phones
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Did You See Bob?:
Human Localization using Mobile
PhonesConstandache, et. al.
Presentation by:Akie Hashimoto, Ashley Chou
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Introduction & Motivation Various research in all
aspects of localization technology Tradeoff between
energy & location accuracy
Indoor localization techniques
Logical location identification
Escort exands the notion of localization in the social context
In large public areas, navigation without precise knowledge of a person’s location can be non-trivial Large & crowded Unfamiliar location
“In a human populated public place, can we develop an electronic system that can localize and route a person A to a specified person B?”
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System Overview Walking trail: <displacement,
direction, time> Unique audio tones
Assimilated global view Routes = sequence of < step i, θi >
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Design ChallengesNoisy Sensors
Location & Trail Errors Encounter Detection
Trail Graph Density Visual Identification
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Noisy SensorsAccelerometer Compass
Double Integration vs. Step Count Method
Average bias of 8 degrees (1) Constant direction
state: compensate stable readings w/average bias
(2) Turning state: use compass reported readings
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Location & Trail ErrorsDiffusion Drift Cancellation
Diffuse fresh location information into system to compensate for drift by…
(1) Encounters with the beacon
(2) Encounters with users who passed the beacon recently
Use diffusion information to correct past trails
Correction vector estimates cumulative drift over time
Assuming projected path deviates linearly, can amortize correction vector over time
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Encounter Detection Bluetooth too slow for
detecting short lived encounters
Clients & beacon employed unique audio tones
Reliability of tone detection tested in 3 scenarios
Transmitter-receiver distance determined via amplitude cutoff (5m threshold)
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Trail Graph Density Phase 1: For every pair of
nodes, closest spatial intersection between them retained; all others eliminated
Phase 2: Graph pruned again to only keep shortest path between users Efficient
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Visual Identification End-to-end: Identify exactly
whom to approach Opportunistically take
pictures of mobile phone’s owner
Generate fingerprint of user’s appearance
Camera-based user identification
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EvaluationTestbed
Limitations & Future Work Related Work
Personal Comments
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TestbedAccuracy Using markers to
show the errors
Sensors (36.2m)Beacon (8.5m)Drift Cancellation
(6.1m)
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Limitations & Future WorkNot energy efficient: sensors and uploading info
to the server Switch off sensors More frequent beacons
Wrong direction – educated guessHidden shortest path – give option for direct pathLow location accuracy – recomputePhone orientation affect sensors – currently more
research on the compass orientationScalability – better or worse
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Related WorkLocation estimated based on the overheard
signals and on the data collected during a calibration. (Beacons and RF)
Using AP and its signal strengthUsing GPS, Wifi and walking pattern to figure out
the location.SLAM robot collecting beacons and landmarks
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Personal CommentsEncounter can cause more errorsCloser to the beacon does not always correlate
to better resolutionEncounter itself has maximum of 5m error
Black spotsSome inside location has no GPS or WiFi.
Beacon must cover all area. Second floor?This paper did not address the possibility of
escorting one to another floor. Are stairs, escalators, and elevators still a possibility?