Bringing navigation indoors

Nokia Research Center

Nokia Internal Use Only

Bringing Navigation Indoors

Fabio Belloni Principal Researcher NRC Radio Systems Laboratory Finland



Contents •  Why going indoors?

•  Use cases, opportunities, and challenges

•  Cognitive Positioning •  Hybrid positioning systems

•  Indoor Map meets Positioning Technologies •  the Positioning Content Layers

•  Angular-based Positioning System •  High Accuracy Indoor Positioning (HAIP)

•  Example of hybrid positioning system •  Combining IMU, HAIP, and indoor maps

•  BLE proximity •  Using standard Bluetooth 4.0 for positioning

Come to see the demos !

2


Nokia Internal Use Only 3

Why going indoors?



Motivation for Indoor Positioning •  Location based services (LBS) are growing

enabled by mobile devices with GPS; e.g. •  Navigation and guidance •  Sport, training, and health •  Social networks •  Security and Emergencies •  Accessibility

•  People spend most of their time indoors •  No wide-spread indoor positioning systems and

services are available yet •  GPS operation is very limited indoors •  Personal communication devices can enable

indoor positioning through local wireless networks

•  Big market opportunity in indoor LBS

People spend 80-90% of their time indoors

70% of cellular calls and 80% of data connections originate from indoors.

(Source Strategy Analytics)

Time spent …

4



Example of Consumer Needs B2C use cases

Who’s around?

Where to find milk?

How to get there? What can I find here?

Where did I leave my car?

Where is the closest

restaurant?

5



Example of Businesses Needs B2B use cases

Is real-time security management possible?

Is the layout of my store effective?

Can I extend the onboard car navigation?

6

I want to track my goods?

How can I attract customers to my

business? I want to build a

dynamic advertisement

platform

Retail chain, Mall manager,

Service Providers, System integrators, Marketing analyst, Car manufactures,

etc.



....mentioning some companies looking at indoors

7

SkyHook Wireless (USA) Qubulus (Sweden) Ekahau (Finland) GloPos (Finland) PointInside (USA) WLAN Tracker (Germany) SenseWhere (UK) SEER Technology (USA) FootPath (Germany) Roodin (Italy) Teldio (Canada) Locata (Australia) OmniSense (U.K.) BatPhone (USA) Q-track (USA) Motorola Google Microsoft Samsung Apple RIM Nokia Sony Ericsson

....the list is not complete and in no particular order....



Indoor environments are very different

8

•  Open/close spaces •  High/low ceiling •  Static/dynamic •  Metal surfaces •  Crowded/empty



Indoor environments are in 3D (….or 2.5D) •  Multi-floors •  Floors opening •  Connection points; elevator,

escalators, staircases, etc •  Usually:

•  3D: Lat/Lon/Alt •  2.5D: Lat/Lon/Floor (or level)

•  Overall Challenge: How to educate the market!

•  For success: having the solid technology solution is necessary…..

…but it may not be sufficient!

9



Cognitive Positioning



Exploiting different Positioning Technologies

Fusion filter

Maps

BT/BLE

IMU

WiFi

GNSS/AGPS

Application



•  Positioning Technologies need to be transparent to users. •  The device need to become positioning aware and switch between positioning

technologies seamlessly. •  Proper handling of transition and handover areas is crucial •  For example:

•  the Application sets the QoS requirements •  The Filter select the most appropriate positioning technology: meet the requirements by using

the least power consumption •  Data Fusion could be carried out using

•  Hard decisions and switching between technologies •  Particle Filter •  Kalman Filter

•  In order to combine independent data streams, it is very important that each estimation technologies provides at least

•  the real-time variance (or any measure of uncertainty) of the generated location data •  Some form of Time Stamp

12

Mobile Devices using Hybrid Positioning



Indoor Maps & Positioning Content Layers



Positioning Content Layer •  Maps comprises of several layers of information, having different roles:

•  For visualization and rendering •  Search •  Routing •  Navigation and guidance

•  The Positioning Content Layer (PCL) comprises of information needed for running the positioning algorithm.

•  PCL are “invisible” to the user.

•  In order to make the whole indoor solution scalable, secure, and sustainable, the PCL need to be referred to one map

•  This should also simply synchronization and data management

14



Positioning Content Layer

15

MAP

•  WLAN •  IMU •  RFID •  BT •  BLE

Finger Printing AP location Radio Maps

Locations of anchor nodes Attributes (e.g. ID, type, MAC addr, Tx channel, etc) Calibration data

Raw map data

Layer with the POI and their attributes

Layer for map rendering and visualization

Layers with information visible to the user

Positioning Content Layers

Classes of positioning technologies

Info enabling specific positioning algorithm

Info used for data filtering

Location of RFID tags Attributes (e.g. ID, type, etc)

Location of BT tags Attributes (e.g. ID, type, etc) Calibration data



– HAIP – High Accuracy Indoor Positioning



HAIP – Location Enhanced Bluetooth Technology •  Built on top of Bluetooth Low Energy (BLE)

technology •  a new protocol allowing direction finding

capability has been added

•  Provides up to 0.3 m position accuracy with <1 sec delays •  Based on directional positioning beacons

installed in covered areas •  No calibration of the radio environment required

•  HAIP can operate in two configuration modes •  Assets Tracking (or network centric) •  Mobile Centric

•  HAIP technologies is based on measuring angular estimation, from or to the locator.

•  The locator is a switched antenna array •  multiple antenna elements and one BLE

transceiver.

17



Two Modes of HAIP – Technical Principle

Network centric mode: •  A Tag or a Mobile Phone transmits

BLE packets at regular intervals •  Locator(s) receive(s) the packet by

using a switched antenna array and provides measurements to a centralized localization server

Mobile centric mode: •  Locator(s) transmit(s) BLE packets at

regular intervals by using a switched antenna array

•  A Mobile Phone receives the packet(s) and calculates its own position

AoA = Angle of Arrival AoD = Angle of Departure

AoA

BLE TRX

Locator

RF switch

BLE TRX

TAG

BLE TRX

Phone

BLE TRX

Locator

AoD

RF switch



Localization Principle with a Single Locator

x

y

φ

θ

hm

z

zm

xm

ym hm = 1.2-1.4 m

Using a single antenna and fixed mobile height, mobile can resolve its 2D location

TX / RX

RX / TX

TX: transmitter RX: receiver

19



Localization Principle with Multiple Locators

x

φ1

θ1

z

zm

xm

ym

z\

x\

φ2

θ2 Using multiple positioning beacons, mobile can resolve its 3D location or increasing the position reliability and accuracy

TX: transmitter RX: receiver

TX / RX TX / RX

RX / TX

20



BLE Tag BLE phone Map + PCL

Positioning Server

Locator (in receive mode)

3GPP WLAN

21

Server-Centric configuration



Locator (in broadcast mode)

Handset with HAIP onboard

Map + PCL Bluetooth Low Energy (BLE) 2.4 GHz Broadcast

3GPP WLAN …. (network connection)

22

One way communication is sufficient between the mobile and the locator; i.e. time synchronization is not needed

Mobile-Centric configuration



HAIP Benefits •  Small power consumption •  Offers high accuracy; ~1 m in large open indoor areas and 0.3m in office space •  No calibration required •  No synchronization required •  Provides clearly better accuracy and reliability than fingerprinting based WiFi positioning

technologies

•  Specific to Server-Centric approach   Tags are small, cheap, and have low power consumption   Tags can be commanded to become active for real-time tracking

•  Specific to Mobile-Centric approach   Beacons may be battery powered and standalone (no network connection required)   Allows unlimited number of mobiles to position themselves   No network dependence => no cost, no latency, no privacy concern   No additional HW required in the phone

23



HAIP Experimental results Real path Measured path Positioning Beacon X

24



HAIP Demonstrations & Videos

25



HAIP Technology Status

•  Current prototypes are built by modifying the Bluetooth Low Energy standard •  Discussions initiated late 2010 about standardizing the technology as part of

future versions of Bluetooth •  BT SIG as standardization proceeding

•  Stage 1 passed; i.e. MRD & FRD (Market and Feature Requirements Document) •  Technical standardization work on going in 2012 •  Estimated availability of chipsets starting in 2013 onwards, presuming successful

standardization

•  Industry consortium under planning to support infrastructure ecosystem creation •  Verification of new business opportunities and creation of joint industry solution •  Pre-commercial pilots during 2012 being planned by Nokia

•  No information currently available on product schedules

26



Example of hybrid positioning system



Pedestrian Dead Reckoning

•  Nokia sensor boxes were calibrated and characterized against commercial MicroStrain IMUs

•  Plain PDR relies on step detection and requires step length calibration for the user

•  But: in hybrid positioning case step length calibration can be handled automatically

TARGET: to evaluate the performance of a Nokia made sensorbox for PDR



Motivation

•  Solutions for outdoor positioning do not directly apply indoors, and other easily deployable approaches are needed

•  With development of MEMS technology and sensors inertial navigation show some promises

•  No infra required fill the gaps

•  Through inertial navigation the distance and heading of the user is tracked from a known initial position and direction

•  therefore inertial navigation alone will not suffice, but complementary aids are needed

MicroStrain 3DM-GX2 www.microstrain.com



Approach

•  Complementary Kalman filter *) tracks the error states of 15 parameters: position, velocity, attitude, gyro and accelerometer biases (all in 3 dimensions)

•  Map information can be taken into account through particle filter

•  Pros: No need for separate step calibration, but it’s ready to go •  Cons: Computational costs

*) See details in E.Foxlin: “Pedestrian tracking with shoe-mounted inertial sensor”, Proc. IEEE Comp. Graphics, 2005.



Results •  Only inertial navigation, no map info used •  Initial position and direction of motion needed

31

Begin & End

Begin

End



Results •  Climbing up the staircase •  Only inertial navigation, no map info used

2nd floor

3rd floor

4th floor

5th floor

1st floor



Fusion filter and hybrid positioning system TARGET: improved accuracy, reliability and reduced complexity of calculations of hybrid positioning system

PDR + HAIP + Fusion filter running on PC



An example of the particle initialization

•  Angle-based location probability matrix covering the room where multi-antenna array is located.

•  The corresponding particle distribution within the room.

34



Fusion filter



Example results

36

Full fusion of map, HAIP and PDR around auditorium

Fusion of map and PDR in cafeteria •  PDR route clearly deviates, but it is corrected by fusion filter •  very detailed location and route shape information in HAIP enabled area thanks to sensor

PDR route

In errorless PDR case this point should be (0,0): now there is about 8 m error after walking around auditorium



Example Results

Using map data in the fusion filter helps improving data quality, at least in a dense indoor environment



BLE proximity Positioning



Concept description •  Applicable on BLE devices: with BLE tags: •  Tags put on known positions and used as anchor nodes •  Only tag address + RSSI is used, data payload arbitrary •  Any BT or BLE device usable

•  Tags used as anchor nodes; e.g. Place one in each room •  The appplication runs on Nokia N9 devices and it uses the BLE

chip already on board of the device to detect the HAIP BLE tags

•  Different algoritms for positioning •  Tag with strongest RSSI (room environment) •  Interpolation for multiple tags (open space)

BLE Proximity Positioning

39



Beneficts

40

•  The system uses standard Bluetooth 4.0 technology

•  Tags •  time life 1-2 years, practically no maintenance needed

•  Easy installation •  They can be hidden without altering the appearance of the indoor environment •  No cabling needed, not even power cable •  Effectively, their installation is as simple as install fire alarms

•  NO system maintenance •  The positioning system does not need to be calibrated •  NO Finger Printing data and/or radio maps (as for WLAN positioning) needed

•  Nokia Research has develop a simple tool allowing anyone to deploy and set up its own BLE proximity positioning system.

•  Part of this work is done in cooperation with NAVTEQ Research where also integration to indoor maps is taken into account



Thank You !

•  F. Belloni, V. Ranki, A. Kainulainen, and A. Richter, "Angle-based Indoor Positioning System for Open Indoor Environments", Proceeding of Workshop on Positioning, Navigation and Communication (WPNC), Hannover, Germany, 2009.

•  P. Kemppi, J. Pajunen, V. Ranki, F. Belloni, T. Rautiainen, ”Hybrid positioning system combining angle-based localization, pedestrian dead reckoning, and map-filtering”, International Conf. on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 2010.

•  P. Kemppi, J. Pajunen, T. Rautiainen, “Use of Artificial Magnetic Anomalies in Indoor Pedestrian Navigation”, Vehicular Technology Conference Fall (VTC 2010-Fall), Ottawa, Canada, 2010

•  Link to video demonstration of HAIP: http://research.nokia.com/news/9505 •  HAIP in 3D configuration: http://www.youtube.com/watch?v=Bpf8xL4g37o

41