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Nokia Research Center
Nokia Internal Use Only
Bringing Navigation Indoors
Fabio Belloni Principal Researcher NRC Radio Systems Laboratory Finland
Nokia Research Center
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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 !
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Why going indoors?
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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 …
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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?
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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?
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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.
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....mentioning some companies looking at indoors
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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....
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Indoor environments are very different
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• Open/close spaces • High/low ceiling • Static/dynamic • Metal surfaces • Crowded/empty
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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!
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Cognitive Positioning
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Exploiting different Positioning Technologies
Fusion filter
Maps
BT/BLE
IMU
WiFi
GNSS/AGPS
Application
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• 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
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Mobile Devices using Hybrid Positioning
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Indoor Maps & Positioning Content Layers
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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
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Positioning Content Layer
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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
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– HAIP – High Accuracy Indoor Positioning
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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.
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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
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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
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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
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BLE Tag BLE phone Map + PCL
Positioning Server
Locator (in receive mode)
3GPP WLAN
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Server-Centric configuration
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Locator (in broadcast mode)
Handset with HAIP onboard
Map + PCL Bluetooth Low Energy (BLE) 2.4 GHz Broadcast
3GPP WLAN …. (network connection)
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One way communication is sufficient between the mobile and the locator; i.e. time synchronization is not needed
Mobile-Centric configuration
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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
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HAIP Experimental results Real path Measured path Positioning Beacon X
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HAIP Demonstrations & Videos
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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
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Example of hybrid positioning system
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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
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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
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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.
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Results • Only inertial navigation, no map info used • Initial position and direction of motion needed
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Begin & End
Begin
End
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Results • Climbing up the staircase • Only inertial navigation, no map info used
2nd floor
3rd floor
4th floor
5th floor
1st floor
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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
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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.
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Fusion filter
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Example results
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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
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Example Results
Using map data in the fusion filter helps improving data quality, at least in a dense indoor environment
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BLE proximity Positioning
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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
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Beneficts
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• 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
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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
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