User-Driven Indoor Visibility Localization with Wayfinding Aleksandar Kuzmanovic

User-Driven Indoor Visibility Localization with Wayfinding

Aleksandar Kuzmanovic

http://networks.cs.northwestern.edu

Aleksandar Kuzmanovic User-Driven Indoor Visibility Localization with Wayfinding2

Motivation - Indoor localization

GPS works outside, not inside

What if you are lost indoors?

Food Mart

?


Triangulation – Delay, Angle, RSSI

RF Signatures– Beacons– Impulse responseInfrastructure

Manual Labor

RF-derived approaches


Some challenges with RF

Food Mart

RF Here RF Here

Not the same!

$$$ - No Thanks


Avoid these obstacles

Leave the building owner out of it– Don’t install infrastructure– Don’t ask for detailed schematics

Cancel site surveys– Don’t build detailed RF maps manually

Use something simple that works– Don’t rely on signal processing


Observations

Many facilities publish free floor plans online

Use pre-computation to extract information People are good noise-filters

Hyatt Regency O’hare


Strategy: Relax constraints, use logic

Let users report what they see

Take advantage of relationships among identifiable features of a room

Absolute precision in x cm is not as important as a person comprehending where he is

OlivesOlives

Self-Serve CounterWine


Important Definitions

Isovist: The visible area from a location’s perspective – Vi,j = {…}, set of coordinates visible from point (i,j)– V’i,j = {…}, set of coordinates invisible from point (i,j)

Feature: An identifiable landmark, e.g. cash register, bathroom, elevator…Feature Vector: [f1 f2…fh], where fi in {0,1}

– fi == 1, fi is visible– fi == 0, fi is invisible

Region: Subset of coordinates sharing an identical feature vector

Isovists G, B, and R

G

B

R


System modeling

Feature: An identifiable landmark, e.g. cash register, bathroom, elevator…Region: Subset of coordinates sharing an identical feature vector

2h possible vectors, so locatable regions grow in O(2h)– Corollary: Average region size decreases as h increases

AB

C

D

AB

C

D

AB

C

D

AB

C

D

AB

C

D

AB

C

DA’B’B’

B’D’

D’

B’C’D’B’D’

C’C’D’

A’C’

A’A’C’

C’C’D’

D’

A’B’

A’B’C’ A’C’

User: I see A and B, but not C or D[A B C’ D’]

You are Here


A simple example: 3 features

If feature A in Vi,j, then point (i,j) in VA

– i.e. if you can see a feature, then that feature can see you.

In general, for all features fp reported visible, and all fq reported invisible:– Location

– 2h (or 2h-1) locatable regions, for h total features in the environment

A

BC

A

B’

BC

B’A’

A’

C’

B’C’

A’B’

C’

A’C’

User: I see A and B, but not C[A B C’]

You are Here


User error

Mistakes happen– Type I Error: Report an invisible feature is visible

• Possible, but rare• Example: confusing “stairs” with “escalator.”

– Type II Error: Report a visible feature is invisible• Not only possible, it’s probable• Our study revealed ~50% hit-rate on noticing features

Assume positive sightings are trustworthy, and negative sightings are completely untrustworthy– Sacrifice all info gained from unsighted features– All information comes from accumulation of positive sightings


Avg. located area vs. h features

Important result– The plausible range of operation (2:5 sightings) performs in line with

the unlimited range of operation

Exponential & Perfect

Accumulative full range

Accumulative up to 5 reports

Accumulative 2 to 5 reports


System in real-life

Internet

Food Mart

?

Company Website

NU Networks Group

What do you see? Check all that apply

Cash RegisterExitEscalator

Submit


Finding your way For some, learning location is enoughFor others, the real goal is a destination– Where is the {dairy section, shoe department, light bulbs}?


The processModel environment as a network

– nodes (features) and – links (intervisibilities)

At each hop, report what you see– app recommends a new next hop

Only adjacent subset of features are offered at each hop

– Makes the list much smaller

If you don’t sight a feature, the link is down

– Ineligible as a next hop


User/App interaction

Based on your input to Part I– I know where you are– More important –

I know what you see

Knowing what you see, I can tell you to walk over to it– Our application picks the best

“next hop” on the way to the destination

– Repeat this in a simple way until the user is within L.O.S. of destination

Application on HTC Android G1


How this is different

Traditional turn-by-turn– Google Maps, GPS, etc.

• “Continue two blocks, turn Right at Main St.” • “Walk down the hallway, turn Right at the Bookcase”

– User must understand semantic instructions• No assurance whether user sees/comprehends• A wrong turn can go unnoticed for a long time• Some people just don’t like thinking this way

Our approach uses collisions to navigate – No wrong turns, because each next-hop has already been

affirmatively identified by the user– User input is a simple Yes/No, rather than semantics (“I see a

red house”) requiring artificial intelligence.


Field Study

Large Hotel/Convention Center (with permission)

– h = 38 features– p = .283 edge density

10 volunteers with no prior knowledge

Test 1 – sighting features– Each subject tested from multiple

vantage points– Positive sightings rate

pv = 0.496, with 90% confidence pv > 0.46

Test 2 – usability– Wayfinding task A B– Tracked user experience

• Willingness to use the tool• Ability to use the tool • Feedback & suggestions


Summary

Users are willing and able to interact with the tool– Requires very little training– Some users eager to find features, like a game– Some users gave cursory effort– The best next-hop sometimes does not feel right, and users

want to override, i.e. take the 2nd best option.

The overall way finding success rate > 95%

Key modeling result– p*pv > (ln h)/h gives good success rate

Documents

User-Driven Indoor Visibility Localization with Wayfinding Aleksandar Kuzmanovic