INDOOR POSITIONING VIA SMARTPHONE SENSING

Associate Professor Tao Gu, RMIT University, Australia

Outline

• Indoor Localization using smartphone magnetometer

• Floor localization

• Localization in Metro Trains

Applications • Shopping malls, office buildings, airports, train

stations, hospitals, trade shows, museums, warehouse, etc.

Smartphone Built-in Sensors

• Use smartphone sensors • magnetometer

• accelerometer

• compass

• gyroscope

• Do not rely on Wi-Fi

• Battery efficiency

• Accurate (1~2.8m )

A Magnetic Fingerprinting Approach to Indoor Localization

Magnetic Fingerprint

Magnetometer Readings

𝑩 = [𝐵𝑥, 𝐵𝑦, 𝐵𝑧] 40 43 44 45 49 48

44 43 46 47 51 47

40 45 48 50 52 48

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37 38 42 43 45 40

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Magnetic Fingerprint: Not Unique!

||𝑩||

Particle Filter

•Particle Motion Tracking

•Particle Weight Evaluation

•Particle Resampling

40 43 44 45 49 48

44 43 46 47 47 47

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-90

Heading changes

Magnetic readings

Initialization

Default Particle Step Length=1

Motion Tracking & Weight Estimation

40 43 44 45 49 48

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-90

Heading changes

Magnetic readings

1

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0 0

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Resampling

40 43 44 45 49 48

44 43 46 47 47 47

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39 42 44 47 49 43

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35 36 40 41 41 38

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-90

Heading changes

Magnetic readings

Motion Tracking & Weight Estimation

40 43 44 45 49 48

44 43 46 47 47 47

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-90

Heading changes

Magnetic readings

1

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Resampling

40 43 44 45 49 48

44 43 46 47 47 47

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Magnetic readings

Get you!

P1: Particle Step Length & User Step Length

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Magnetic readings

Default Particle Step Length=1

User Actual Step Length=2

User Actual

Position

Estimated Position

P2: Localization Failure - Kidnapped Robot Problem

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Heading changes

Magnetic readings

We cannot realize the localization has failed until all particles’ weight become 0.

User Actual

Position

Estimated Position

1

P3: Heading Offset Change

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Heading changes

Magnetic readings

User’s Heading direction

Phone’s Heading direction

User answer a Call

User Actual

Position

Estimated Position

P4: Calibrating Different Smartphones ?

Our Contributions

• Dynamic step length estimation

• Kidnapped robot problem

• Heading offset change

• Calibrating different smartphones

Real Experiments in an Office Building

Accuracy

1-2.8 m

Smartphone Battery

Floor Localization

• Motivation: • Do not rely on Wi-Fi

• Leverage on smartphone sensing and crowdsourcing

• Applications: • Location based services

• Emergency services

Traveling Time

Step Count

Acceleration

Bluetooth

Encounter

Acceleration

User Trace

Cloud Server

Stairs

From Floor

To Floor

Traveling Time

Step Count

1 2 8 s 22

1 3 12 s 44

Elevator

Mapping table

Download

User Trace

From Floor

To Floor

Traveling Time

Step Count

1 2 8 s 22

1 3 12 s 44

The Initial floors of users

Escalators

Localization in Metro Trains

• Problem definition: how to locate mobile users in a metro train?

• Motivation: • Do not rely on Wi-Fi

• No need to war-drive all the metro lines

• Smartphone sensing • accelerometer

• magnetometer

• barometer

• Crowdsourcing

Structure Infrastructure Movement

Metro Line

Traditional Indoor Spaces

Different from Traditional Indoor Spaces

Limited infrastructure

Low speed Random

High speed Predictable

Abundant

Simple

Complex

11/2/2015

Logging accelerometer, barometer reading. Do train stop event Recognition.

Localization map

Trace merging Pattern map generation

Cloud Server

Metro Train

A B C

Crowdsourcing

User traces 1

location

Stop event Recognition. Running event matching.

2

More Information • My website

• https://sites.google.com/site/gutao98/home

• Related Publications • Hongwei Xie, Tao Gu, Xianping Tao, and Jian Lu. A Reliability-Augmented Particle Filter for

Magnetic Fingerprinting based Indoor Localization on Smartphone, IEEE Transaction on Mobile Computing (TMC), 2015.

• Hongwei Xie, Tao Gu, Xianping Tao, and Jian Lu. MaLoc: A Practical Magnetic Fingerprinting Approach to Indoor Localization using Smartphones, In Proc. of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014), Seattle, Washington, September 13-17, 2014.

• HaiboYei, Tao Gu, Xianping Tao, and Jian Lu. SBC: Scalable Smartphone Barometer Calibration through Crowdsourcing, In Proc. of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2014), London, December 2-5, 2014.

• HaiboYei, Tao Gu, Xianping Tao, and Jian Lu. B-Loc: Scalable Floor Localization using Barometer on Smartphone, In Proc. of the 11th IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS 2014), Philadelphia, Pennsylvania, October 28-30, 2014.

• HaiboYei, Tao Gu, Xianping Tao, and Jian Lu. Crowdsourced Smartphone Sensing for Localization in Metro Trains, In Proc. of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2014), Sydney, Australia, June 16-19, 2014.

Opportunities

• R&D Centers • Joint research and development

• Joint funding

• Industry Partners • Commercial Product development

• Australian Government Funding • Collaboration between an Australian university and an industry

partner (local or overseas).

• http://www.arc.gov.au/linkage-projects

• Venture Capital for creating a start-up

11/2/2015