Bringing navigation indoors

Bringing Navigation
Indoors

Fabio Belloni
Principal Researcher
NRC Radio Systems Laboratory
Finland
Nokia Research Center
Nokia Internal Use Only

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


Why going indoors?

3


Motivation for Indoor Positioning
•  Location based services (LBS) are growing
enabled by mobile devices with GPS; e.g.

Time spent …

•  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
4


People spend 80-90% of their
time indoors
70% of cellular calls and 80% of
data connections originate
from indoors.
(Source Strategy Analytics)

Example of Consumer Needs
B2C use cases

Where to
find milk?

Who’s
around?
Where did I
leave my car?

Where is the
closest
restaurant?

How to get there?

5


What can I
find here?

Example of Businesses Needs
B2B use cases
Is real-time security
management
possible?

Is the layout of my
store effective?
Retail chain,
Mall manager,
Service Providers,
System integrators,
Marketing analyst,
Car manufactures,
etc.

How can I attract
customers to my
business?

I want to track my
goods?

6


Can I extend the
onboard car
navigation?

I want to build a
dynamic
advertisement
platform

....mentioning some companies looking at
indoors
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

7


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

Indoor environments are very different

•  Open/close spaces
•  High/low ceiling
•  Static/dynamic
•  Metal surfaces
•  Crowded/empty
8


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

10


Exploiting different Positioning Technologies
GNSS/AGPS

WiFi

BT/BLE

Fusion filter

IMU

Maps

11


Application

Mobile Devices using Hybrid Positioning
•  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


Indoor Maps &
Positioning Content
Layers

13


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 LayerFinger Printing
Classes of

Info enabling

AP location
Radio Maps

positioning
technologies
•  WLAN

specific positioning
algorithm

Info used for data filtering
Location of RFID tags
Attributes (e.g. ID, type, etc)

•  IMU
•  RFID

Attributes (e.g. ID, type, etc)

•  BT
MAP

Location of BT tags
Calibration data
Locations of anchor nodes

•  BLE

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

Layer for map rendering and visualization
Layer with the POI and their attributes
Raw map data
Layers with information visible to the user
Positioning Content Layers

15


– HAIP –
High Accuracy Indoor
Positioning

16


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:

Mobile centric mode:

•  A Tag or a Mobile Phone transmits
BLE packets at regular intervals

•  Locator(s) transmit(s) BLE packets at
regular intervals by using a switched
antenna array

•  Locator(s) receive(s) the packet by
using a switched antenna array and
provides measurements to a
centralized localization server

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

Locator

TAG

Locator

Phone

AoD

AoA
RF
switch

RF
switch

BLE TRX

BLE TRX

BLE TRX

AoA = Angle of Arrival
18


BLE TRX

AoD = Angle of Departure

Localization Principle with a Single Locator
z
Using a single antenna and

TX / RX

fixed mobile height, mobile
can resolve its 2D location

zm
hm = 1.2-1.4 m

θ

hm

xm

ym

φ

y

RX / TX

TX: transmitter

x

RX: receiver

19


Localization Principle with Multiple Locators
z

z
TX / RX

TX / RX

θ2

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

zm

θ1

reliability and accuracy

xm
x

ym
RX / TX

φ1

φ2
TX: transmitter

x

20


RX: receiver

Server-Centric configuration
Locator
(in receive mode)

Positioning
Server
3GPP
WLAN

BLE Tag
BLE phone

Map + PCL

21


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

Locator
(in broadcast mode)

Map + PCL

Bluetooth Low Energy (BLE)
2.4 GHz Broadcast

3GPP
WLAN
….

Handset with HAIP onboard

(network connection)
22


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

27


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

•  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

28


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

MicroStrain 3DM-GX2
www.microstrain.com

•  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


29

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.
*)


30

Results

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

Begin & End

End
Begin

31


Results

•  Climbing

up the staircase

•  Only inertial navigation, no map info used
5th floor

4th floor

3rd floor

2nd floor

1st floor

32

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

33


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

35


Example results
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

Full fusion of map, HAIP and PDR
around auditorium


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

36


Example Results

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

37


BLE proximity Positioning

38


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)

39


Beneficts
•  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
40


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


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