AirLoc: Mobile Robots Assisted Indoor Localization

IEEE MASS 2015

Chen Qiu and Matt W. Mutka

Dept. of Computer Science and Engineering Michigan State University

Outdoor Localization Indoor Localization

Ultrasound

WSN

RFID

Smartphone

GPS

Location Based Services

Smart HomeShopping Mall

Cleaner Hospital

Simultaneous localization and mapping (SLAM)

TurtleBot

What does the world look like?Where am I? Sensing Mapping Filtering

Constraint: can Not locate people’s positions

Everyone has a smartphone

Dead Reckoning

S1S2

SnSn−1a x X

Y

Z

ay

az

Smartphone Based Indoor Localization

Smartphone’s Acceleration Not Equal to User’s body Acceleration

Error Accumulation

0.5 degree error of orientation sensor 308m error within 1 minute

Imperfection

Conjecture: Could mobile robots assist Indoor localization ?

Smartphone based indoor positioning• Dead Reckoning • Finger Printing / Radio Map • Other Inertial Sensing (Light, Sound, Barometer)

Mobile Robot • Low cost (TurtleBot) • Accurate Position (Error within 0.3m) • Extra Services

0 5 10 15 20 25 30 35 400

10

20

30

40

50

Time (seconds)

Dev

iatio

n D

ista

nce

(met

ers)

Confidence intervalConfidence intervalDeviations without calibrationDeviations with calibration

Receive AccurateLocation Message

(X,Y)&

(X,Y)& (X,Y)&

(X,Y)&

Building Connection

RSSI>Threshold

Scanning Devices

Sending Location Messages

Preliminary Observation

Sending Accurate Location Information

Goal of System Design: Mobile Robot send more accurate location to smartphones

Serving Routes Selection: First Serve the places with more people

More Robots, Fast Robots: System Costs, Equipments limitation

Sensing Range: Bluetooth (~10m), WiFi (Energy Concern)

Potential Resolutions:

Sampling Frequency: Cannot Define on Smartphone, Energy Consume

Crowd Density Estimation

Common Phenomenon: RSSI variation caused by human bodies

3. density levels for generating routes

1. collect features on each sub-grid

High Density Level

Normal Density Level

Low Density Level

2. cluster samples

Steps of Estimation:

Feature 1: Num of Devices Feature 2: Bluetooth RSSI

K-Means EM

First Serve Higher Density

Problem Formulation

Node

Edge

Room

Hallway

GraphIndoor Map

Robot’s Traveling Strategy

Traveling Salesman Problem (TSP): • Find the best way to visit all the cities • Minimal travel time • NP-hard Problem

AirLoc focuses on time cost of routes and rooms

Edge Based Algorithm (EBA): • Both edges and nodes are assigned weights • Dynamic Programming to find the route with minimal travel time • Approximate solution (NP-hard Reduction)

Edge Based Algorithm (EBA)

R1(HD) R5(LD)

R4(HD) R3(LD)

R2(MD)

1.5min

2min 0.5m

in

1min

LD – Low Density

0.5min 0.5min

0.5min

0.5min

0.5min

0.5min

MD – Medium Density HD – High Density

– Delete after round 1 – Delete after round 2

Parallel Function 1: Computing Serving Routes

Density Levels

Serving Routes

Clustering Dynamic

Programming

RSSI Collection

(X,Y)&

(X,Y)& (X,Y)&

(X,Y)&

Moving Robot

Known Map

Mobile APP

Parallel!Function 2: Sending Accurate Location Information

Building Connection

RSSI>Threshold

1. Bluetooth RSSI 2. Number of Devices

Scanning Devices

Sending Location Messages

Overview of Single Robot Based System

Multi-robots System Design

Single robot is not enough

Environment with more rooms

Crowd density is dynamic

More robots, better accuracy

Multi-robot design strategy

Two-robots System Design

Partition the graph to two components

Principle: allocate more time to higher density rooms

Trade off between Distance and Density

Principle: balance between the two aspects

Principle: limit the time costs on the edges

Density First Algorithm (DenFA) Distance First Algorithm (DisFA)

Distance/Density First Algorithm (DDFA)

1

00

2

1

8

8

9

Density First Algorithm (DenFA)

X axis

Y ax

is

Merge to Low Density Area

1

00

2

1

8

8

9

Distance First Algorithm (DisFA)

X axis

Y ax

is

Merge to High Density Area

Distance/Density First Algorithm (DDFA)

Combine DisFA and DenFA

Keep Connectivity in each component

Use thresholds to assign weights

T1 and T2 , Distance First

T1 and T2 , Density First

Low Density AreaHigh Density Area

nLDAnHDA

≤10%

Preemption Period:

21%

2n%

22%23%

nLDAnHDA

>10%

Exponential growth

Preemption: make robots more efficiency

Low Density AreaHigh Density Area

Preemption Period:

nLDAnHDA

>10%

Return to Initial State

20%

Preemption: make robots more efficiency

Extend two robots to multi-robots

- Higher Crowd Density Area

Unbalance Serving Tree

Number of Devices

Aver

age

RSS

I (%

)

Room i

Allocate robots to HDA(P ×θ ) / (θ +1)

Allocate robots to LDAP − (P ×θ ) / (θ +1)

θ = ( ω ii=1

H

∑ ) / ( ω jj=1

L

∑ )

Definition of ω

P - num of robots

Extend Two Robots to Multi-robots

How robots go back to the root ?

Return by the way you came

Dynamic Return Approach

Find node k: the smallest sum of distances between k and other rooms

Arrange k as the “new” root

min( Distj=1

n

∑i=1

n

∑ (i, j))

crowd density change, waste time

AirLoc System Evaluation

Experiment Setting

•The height of the tablet is 1 meter

•The speed of is 0.3m /s

•0-6 volunteers carry Samsung Galaxy 4 or Google Nexus Tablet

•Employ on Bluetooth Adapter to communicate

•Volunteers in the experimental environment walk freely

MetricsDeviation Distance: Euclidean Distance (meter)

L(x) = − P(xi )log2(P(xi ))i=1

m

∑Location Entropy:

Data Collection in Indoor Building

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Data collected in a room

Data collected in a hallway

- Robot Calibration - Smartphone Position

Data samples on the map

1

2

3 1

4

5

7

8

9

10

11

12

13

14 15

16

17

2

3

4 5

8

7

6

10 11 12

15 14

13

16

18

19

21

20

22

24

23

25

17

Ground Truth

Estimated Trace

20 19

18

23

22 21

25 24

R1

R1

R2

R1 R3

- Send Location Message

- Sequence Num of Robot R#

- Calibrate Deviation

1100’s Floor Plan

R3 Cloud Server

9

Single Trace Study in Real Floorpan

• Calibrated by robots, the errors are reduced• Dead reckoning yields obvious deviations

• Mobile robots update the crowd density in a cloud

Conclusion:

T Slot S Slot R Slot

T Slot

… …

Update Density Levels

Form Final Serving Area Divide Groups

Loop

OPOS (One Period One Sample): T Slot OPMS (One Period Multiple Sample): T Slot & S Slot

Static crowd density: initial state

Crowd Density Updating

Conclusion:• Multi-robots update crowd density continuously• Real Time crowd density improves the localization results

Duty cycle of AirLocCompare different density information

2 4 6 8200

300

400

500

600

Number of rounds

Nu

mb

er

of

d

evi

atio

n g

rid

s

OPMS crowd density

Static crowd density

OPOS crowd density

0 2 4 6 8 10 120

0.2

0.4

0.6

Number of rounds

Lo

catio

n E

ntr

op

y

BalanceTree−Dynamic Return

BalanceTree−Static Return

UnblanceTree−Static Return

UnblanceTree−Dynamic Return

1 2 3 4 5 60

50

100

Number of smartphones

Ave

rage R

SS

(%

)

Low Density Level

High Density Level

Normal Density Level

Centroids of clusters

1 2 3 4 5 6

100

200

300

400

500

Average degree of nodes

Nu

mb

er

of

d

evi

atio

n g

rid

s

32 Robots 8 Robots EBA

Evaluation Results

Cluster different density levelsfor all the rooms

Unbalanced Tree outperforms Balance Tree

Dynamic Return enhances AirLoc

More robots provide more accuracy

Summary• Mobile robots interact with smartphones to send

accurate location information

• AirLoc organizes multi-robots for improving the smartphones’ positioning information

• Single robot adopts Edge Based Algorithm (EBA) to generate the optimized serving route

• AirLoc updates the crowd density levels continuously

Distance/Density First Algorithm Dynamic Return

PreemptionUnbalanced Serving Tree

Thank you !

Questions ?