Did You See Bob?:

Human Localization using Mobile

PhonesConstandache, et. al.

Presentation by:Akie Hashimoto, Ashley Chou

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?”

System Overview Walking trail: <displacement,

direction, time> Unique audio tones

Assimilated global view Routes = sequence of < step i, θi >

Design ChallengesNoisy Sensors

Location & Trail Errors Encounter Detection

Trail Graph Density Visual Identification

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

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

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)

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

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

EvaluationTestbed

Limitations & Future Work Related Work

Personal Comments

TestbedAccuracy Using markers to

show the errors

Sensors (36.2m)Beacon (8.5m)Drift Cancellation

(6.1m)

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

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

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?