1

Location-Sensing andLocation-Based Services

Yu-Chee Tsengcs.nctu.edu.tw

2

Reading #1

3

Location: Mobile Computing’s FutureLBS = location-based servicesExamples:

where you arehow best to get to a destinationwhether friends are nearbylocal weather forecastwhere businesses of interests in this area are

locatedcompanies to track packages, vehicles, buese, etc.US 9-1-1 emergency localization by 2012

(week 1)

4

Inside LBSGPS devices: Garmin,

Magellan, and TomTom all sale dedicated GPS devices

Hardware: chip makers (such as Qualcomm’s gpsOne chipset)

Software: Google’s navigation service

5

LBS 服務分類 封閉系統 :

不具有無線資料傳輸能力，以 GPS 為代表 但是如果有臨時的道路封閉或臨時的車禍路況，就無法

即時反映在系統上 行動位置服務（ Mobile Location Based Service,

MLBS ）系統 :以 GSM 、 3G 或 WiFi 為資料、語音甚至多媒體的通

訊管道、並同時具有定位功能如手機的緊急求救可同時回傳位置資訊，爭取救援時效

6

Augmenting GPS with Cellular and WiFicell-based or triangulation-basedexamples:

Skyhook has LBS software to determine location via triangulation of cellular and WiFi signals

XPS: hybrid positioning system using 50 million WiFi APs to enhance accuracy

Google’s My Location: using database of cell tower positions WiFi triangulation

Microsoft may include LBS capabilities in its upcoming Windows 7.

7

GPS + WiFiGPS 導航定位功能與日常生活越來越密切，除了車載導航，許

多手機都開始內建 GPS 或 A-GPS 功能 .在個人行動應用的領域，當使用者進入騎樓、巷弄或室內時，

無法清楚收到 GPS 衛星訊號，定位功能無法發揮作用，必須尋求其他的輔助方案。

Wi-Fi 定位技術有兩種型態 一為運用已佈建完成之無線網路環境 另一種是無線網路隨同定位系統一起佈建之環境 (more costly)

龍頭大廠 Broadcom 推出升級版的行動定位服務（ LBS ）架構 , 將 Wi-Fi 定位功能增至 LBS 組合中 藉由偵測 Wi-Fi 無線存取點並與一個己知的地點位置資料庫比對

產生精確的定位資訊

8

Reading #2

9

Classification of Localization Methods

(week 3)

10

Cell-Identification 是最基本的行動定位技術，利用行動終端連線時所處

之基地台位置來確認用戶端位置定位準確度取決於基地台涵蓋面積及密度。

在鄉村地區，基地台稀少覆蓋範圍大，所以定位準確度很差；

而在都市地區，基地台覆蓋範圍較小，且密度較高，定位準確度相對提高許多

平均大約為 200 公尺至 2 公里。

11

Tri-angulation ( 三角定位 )利用訊號蜂巢交叉點的定位技術，當行動終端收到基

地台訊號，利用其強度計算行動終端與基地台距離，並以此距離為半徑畫出一個覆蓋圓弧 , 畫出 3 個覆蓋圓弧，其交接點處即為行動終端位置

12

TOA （ Time of Arrival ） 基於訊號傳輸時間的定位技術

需調整基地台設置，讓其時間可以同步，使相鄰基地台能夠同時監控同一行動終端的信號，隨著基地台個數增加其準確度也會提升。

TOA 基於測量信號從行動終端發送出去並到達訊號測量基地台（ 3 個或更多）的時間，並將此時間轉換成距離，畫出各基地台的覆蓋圓弧，取其焦點即為行動終端位置。

為了使時間誤差不會對定位效果造成影響，基地台之間的訊號傳輸同步顯得相對重要，即使是 1 微秒的時間誤差，也會導致兩三百公尺的誤差。

13

TDOA (Time Difference of Arrival)TDOA也是基於訊號傳輸時間的

定位技術利用一個參考基地台與多個輔助

基地台收到訊號到達的時間差，再將之轉換成距離

利用雙曲線的特性，即雙曲線上的點到兩焦點距離之差為定值，帶入雙曲線的方程式中，多組雙曲線方程式聯立求解，即得到行動終端位置。

容易實現，且行動終端與基地台間無需保持精確同步

14

RSS 定位 RSS=Received Signal

Strength訓練階段 : 定位原理是行動終

端利用無線網路來收集無線信號，並藉由電播傳播模型或內外差法，得到其它收集訊號的區域，得到無線電波圖 (radio map)

追蹤定位階段 : 行動終端得到的訊號與電波圖比較，計算與模擬電波圖上相似機率，以機率最高處為行動終端位置。

優勢 : 可用於室內 , 大樓的各樓層

15

技術分類

代表性廠商 準確度 優點 缺點

Wi-Fi(RSS)

ITRI, Ekahau, Skyhook, Intel Research

室內： 1 ～5m室外： 20 ～40m

室內外皆可使用，準確度高，純軟體方案，支援標準 WiFi AP ，可判斷樓層資訊，不需更動網路設備

•開闊空間準確度較差 •需事先對環境做過校正 •環境變動會影響準確度

Wi-Fi(TDOA)

AeroScout, Hitachi AirLocation

1 ～ 5m 準確度較高 •需要專屬的網路硬體設備

Cell ID   250 ～ 1000m 不需更新現有網路系統與手機 •準確度差

A-GPS SnapTrack, SiRF

20 ～ 50m 開闊地區的準確度高，系統設備成本低

•手機需更新硬體 •手機成本高 •手機的耗電量高

TDOA TruePostion 50 ～ 150m 適用於各種手機 •系統設備成本高 •設備需做精準時間校正

16

Reading #3

17

10 Applications of LBS10 applications that make the most

of location (Wired Magazine, www.wired.com, 17.02)

(week 2)

18

1. Drive Fast, Avoid CopsTrapster (iPhone, BlackBerry)

report red-light cameras, speed cameras, cops hiding, …

19

2. Sleep Easy, We’ll Wake You UpiNap (iPhone)

Linked to Google map. As long as your GPS gets signal, it will wake you up at designated destinations.

20

3. Target and Tag Location GameJOYity

(Android)3 location-

savvy games (e.g., when a tag is within 80 feet, press trackball on your phone to execute the tag)

21

4. Call a CabCab4Me (Android)

5. Scan a Barcode, Find a DealShopSavvy (Android)

help you to find a cheaper deal somewhere else

6. See the World, Google’s EyesGoogle Earth (iPhone)

22

7. Train Your Phone to Know Its PlaceLocale (Android)

turn off your ringtone at programmed placescan also change your wallpaper and disable

WiFi at programmed areas.

23

8. Look Up! Be a Stellar Student.GoSkyWatch

(iPhone)to get the name

of a star, just point the back of your phone toward it (thanks to iPhone’s accelerometer)

24

9. Dark Alley? Call for Help.SafetyNet (Android)

Use its map to select bad neighborhood areas. When you are in these areas, your mobile

phone will enter watchdog mode.If trouble arises, just “shake” the handset, and

your phone will start sending your location to your friends/families every 30 sec., take a picture, turn on speaker, and dial 911.

25

10. Find Public Bathrooms!SitOrSquat (iPhone, BlackBerry)

26

RADAR for Indoor Localization

(week 2)

- 27 -

Introduction

Positioning techniques can be categorized into Range-based: triangulation, trilateration, multi-lateration, pattern-

matching localization Range-free: use the information of hop counts, zone-based

localization

Distance/angle/pattern measurement Distance: time-of-arrival (ToA), time-difference-of-arrival (TDoA),

signal strength attenuation Angle: angle-of-arrival (AoA) Pattern: signal strength

- 28 -

Pattern-Matching Localization Overview

<x1, y1> 1

<x2, y2> 2...<xn, yn> n

LocationDatabase

Pattern-MatchingLocalizationAlgorithm

<x, y>

Training Phase Positioning Phaseavg. signal strength:[ i,1, i.2,…, i.m]

trainingdata

signal strength vector: [s1, s2, …, sm]

s

sreal-time

data

training location

access point (AP)

<xi, yi>i

<x1, y1>

<x2, y2>

<xn, yn>

1i

- 29 -

Challenges with Pattern-Matching Localization

Unstable signal strengths and unpredictable multipath effect

High computation cost: huge location database to match, especially in large-scale environments

Environment changes and training cost Maintenance (movement/lost of beacons) Publications

S.-P. Kuo, B.-J. Wu, W.-C. Peng, and Y.-C. Tseng, "Cluster-Enhanced Techniques for Pattern-Matching Localization Systems", IEEE Int'l Conf. on Mobile Ad-hoc and Sensor Systems (MASS), 2007

S.-P. Kuo, Y.-C. Tseng, and C.-C. Shen, "Increasing Search Space for Pattern-Matching Localization Algorithms by Signal Scrambling ", IEEE Asia-Pacific Wireless Communications Symposium, 2007.

S.-P. Kuo, Y.-C. Tseng, and C.-C. Shen, "A Scrambling Method for Fingerprint Positioning Based on Temporal Diversity and Spatial Dependency", IEEE Trans. on Knowledge and Data Engineering, submitted.

S.-P. Kuo, H.-J. Kuo, Y.-C. Tseng, and Y.-F. Lee, "Detecting Movement of Beacons in Location-Tracking Wireless Sensor Networks", IEEE VTC, 2007-Fall.

30

Reading #4

31

The BikeNet Mobile Sensing System for Cyclist Experience Mapping

Shane B. Eisenman**, Emiliano Miluzzo*, Nicholas D. Lane*Ron A. Peterson*, Gahng-Seop Ahn** and Andrew T. Campbell*

*Dartmouth College, **Columbia University

Sensys 07

32

BikeNet System Overview

33

Social Network Shared Data

BikeNet

Air Quality

CoastingNoise

Distance

Braking

Car Density

34

System Architecture

• Hardware– Mobile Sensor Tier

• Logical Bike Area Network (BAN)

– Sensor Access Point Tier• Static v.s. Mobile • An unmodified Tmote Invent plugged into the USB port of an

Aruba AP-70 IEEE 802.11a/b/g access point• Nokia N80 paired to a custom built Bluetooth/802.15.4

gateway (GSM/GPRS Server Tier)

– Server Tier• Backend server: Query and Visualization

The Sensing System

BikeNet niclane@cs.dartmouth.edu

Physical Bike Area Network (BAN)

The Sensing System

BikeNet niclane@cs.dartmouth.edu

Logical Bike Area Network (BAN)

37

The Sensing System

BikeNet niclane@cs.dartmouth.edu

Backend Services

39

System Evaluation

Health index = 1.0 − a1 CarDensity∗ − a2 CO∗ 2Level − a3 SoundLevel.∗

Performance index = b1 HillAngle + ∗ b2 WheelSpeed/PedalSpeed +∗ b3 Distance.∗

40

Performance IndexDistance

Duration

Speed

Path Slope

Coasting

Per f . = b1 HillAngle+b2 WheelSpeed/PedalSpeed +b3 Distance.∗ ∗ ∗

41

Health IndexNoise

C02 Level

Traffic Density

Health = 1.0−a1 CarDensity−a2 CO2Level −a3 SoundLevel.∗ ∗ ∗

42

43

44

45

BikeView: Present and Share

46

Public Utility Sensing: CO2 Map ~ Hanover NH

47

Conclusions

• BikeNet represents the first comprehensive mobile sensing system quantifying the cyclist experience.– Performance/Fitness/Health

• Personal sensing + Social sensing

- 48 -

Indoor Wireless Localization

Signal scrambling (IEEE TKDE) Data clustering (MASS 2007) Beacon movement (VTC 2007, IEEE

TMC)

(week 4)

- 49 -

Localization:

Signal Scrambling

A Scrambling Method for Pattern-Matching Positioning Based on Temporal Diversity and Spatial Dependency

- 50 -

Difficulties

Multipath effect results in low accuracy for pattern-matchinglocalization.

Most of pattern-matching localization schemes adopt traditional classification, but ignore some unique features. Ex. Continuous samples should have high

similarity as well as diversity.

b1

b2 b3S2 S3

S1

l1

b1

b2 b3S3

S1

S2 * l3

b1

b2 b3S2

S1

S 3*

l1

l2

- 51 -

Observations

A positioning error could be generated by a small portion of interfered signal strengths. Counting on one single observation is unreliable. We can enlarge the search space by multiple

continuous observations.

Continuous observations may have some degrees of Temporal diversity: For a sequence of

observations on a beacon, diversified signal strengths may be seen.

Spatial dependency: For a serious of estimated locations, they should be close each other.

- 52 -

Signal Scrambling Concept

Select a set of beacons (access points) for scrambling Ex. top =2 beacons with strongest signal

strengths Average the signal strengths of the other beacons Generate w

scrambled samples from

the past w=2

samples.

2 signal vectors

3 beacons

b1 b2 b3

Inpu

ts3s1 s2

* s1s1 s2 s2s3*

s3s1 s2 s3

s4s1 s3*s2

*

new searchspace

scrambled beacons

- 53 -

Localization:

Clustering of Location Database

for pattern-matching localization in large-scale sensing field (such as a wireless city)

- 54 -

Challenges

Scalability problem when the field is large. High computation cost in the positioning phase Long system response time (critical to real-time

applications)

To reduce computation cost in the positioning phase: apply clustering technique to fragment database

into a number of sets. examine only one cluster in the positioning phase

- 55 -

Cluster Scheme Overview

<x1, y1> 1

<x2, y2> 2...<xn, yn> n

LocationDatabase

Pattern-MatchingLocalizationAlgorithm

<x, y>

C*

Training Phase Positioning Phase

signal strength vector: [s1, s2, …, sm]

avg. signal strength:[ i,1, i.2,…, i.m]

trainingdata

s

sreal-time

data

Clustering

training location

access point (AP)

<xi, yi>i

<x1, y1>

<x2, y2>

<xn, yn>

1i

- 56 -

RSS of AP 1

RS

S o

f A

P 2

<x1, y1>(1,1, 1,2)

2

1 3

appears at <x1,y1>

Considering 2 APs in the environment

C2

If s is in theshaded region

1,1: received signal of AP 1 at <x1,y1>

1,2: received signal of AP 2 at <x1,y1>

Real-time receivedsignal strengths s

Cluster C1

Cluster C2

Cluster C3

Cluster C2

The region that the signal may fluctuate

Cluster C3 The cluster that contains the true location

≠

False cluster selection occurs!!!

C1

C2

C3

1

2

3

Difficulty: False Cluster Selection

- 57 -

Clustering Allowing Overlaps

Design new clustering techniques that allow a training location to join multiple clusters constructed by k-means. overlapping degree : the number of

clusters that a training location can join.

Complexity:

If C2 also contains <x1, y1>, the false clusterselection problem can be avoided

without overlaps(k-means)

with overlaps

average overlappingdegree

Considering 2 APs in the environment

Cluster C1 Cluster C3

Cluster C2

RSS of AP 1

RS

S o

f A

P 2

<x1, y1>(1,1, 1,2)

Cluster C2

| |( )O k

kL | |

( )O kk

L

- 58 -

Localization:

Beacon Movement Detection

- 59 -

Beacon Movement Detection Problem Maintenance issue: beacon movement/failure Ex: What happens if some beacons are moved by accident?

Goal: Automatically detect the beacon movement events Remove the data of these unreliable beacons from the database to

improve accuracy

b2

b3b1

b3

Detected LocationReal Location

(Moved)

d

d

Result: More serious localization error!!

- 60 -

Challenges

Ambiguity

More Ambiguity:

b1

b2 is moved!

b1

b2 b3

=b1

b2 b3

Two different scenarios will induce equal observations!

b2

b1 is moved!

O = O’

- 61 -

System Model

1 2: observation matrix ( , ,..., ) at time t t t t TnO O O O t

( t =0 denotes the initial time)

Positioning Engine

Wireless Sensor Network

LocalizationAlgorithm

LocationDatabase

b1

bnb9

b8

b7

b6

b5

b4

b3

b2

Calibration Algorithm

BMD Engine

S

S

S

Sot

14

ot13

ot12

ot15

Positioning Procedure

BMD Procedure

ot14

ot13

ot12

ot15

...

Ot1, …,Ot

n

Otn

Ot5

Ot1

......

Ot1, …,Ot

n

Otn

Ot5

Ot1

... B’B’

Positioning Engine

LocalizationAlgorithm

LocationDatabase

Calibration Algorithm

Data of B’

Update the database!

(remove the data of B’ from DB)

- 62 -

Four Movement Detection Algorithms

Neighbor-Based SchemeLocally decide if the neighboring beacons are

moved based on the neighborhood relation

Signal-Strength Variation SchemeSimilar to the first scheme. Local decision is made

according to the observed signal-strength changes.

Signal-Strength Summation SchemeThe BMD engine sums up the reported signal

strength changes

Location-Based SchemeUse a positioning technique to compute each

beacon’s location

b1

b2

o12 = Δs

b1

b2

o12 = 0 or 1

o12 = snow

b1

b2

63

Reading #5

64

Localization by RFID

ref: “LANDMARC: Indoor Location Sensing Using Active RFID”, PerCom’03by: L. Ni, Y. Liu, Y. C. Lau, and A. P. Patil

65

Some Thought

Solution 1: deploying many readers

Solution 2: deploying many tags

66

Goal of This Work

to investigate whether the RFID technology is suitable for locating objects with accuracy and cost-effectiveness.

LANDMARC: LocAtioN iDentification based on dynaMic Active RFID Calibrationfor in-building use.utilizing the concept of reference tags.

67

LANDMARC Architecture:1. readers2. fixed tags3. tracking tag

68

LANDMARC Approach (I)

In the sensing field: n readers m fixed tags u tracking tags (attached to a moving object)

Readers are configured with continuous mode. Detection range = 1 ~ 8. Signal Strength Vector of a tracking tag:

S=(S1, S2, …, Sn) Signal Strength Vector of a fixed tag i:

Fi=(θ1, θ2, …, θn)

69

LANDMARC Approach (II)

Euclidian distance between a tracking tag and the i-th fixed:

Location of the tracking tag:• pick the k fixed tags with the smallest “Euclidean

distances”• weighted location:

2

1( )

n

ji

i iE S

1

( , ) ( , )k

i i ii

x y yw x

2

21

1

1ki

i

i

i

Ew

E

70

ExperimentEnvironment

71

Environmental Factors: Daytime vs. Night

Do not see much difference in the overall accuracy.

72

Effect of n (Number of Readers)

With more RF readers, a better decision can be made.

74

Reading #6

75

Oliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

Pedestrian Localisation for Indoor Environments

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

in Ubicomp 2008

76

Related Work

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

lδθ

77

System Overview

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

• Our system combines consists of two filters1. A PDR filter

• We use an existing PDR technique2. A particle filter for localisation

• Similar to those developed for robot localisation, except:1. Robots don’t climb stairs, but pedestrians do - Need to extend to 3D2. In existing literature, robot localisation is usually tested in a relatively small

area. We deploy our system in a large (8725m2) three-storey building

PDR Filter

Localisation Particle Filter2.5D Map

(a,ω)

Steps (l,δθ,δz)

Absolute positions (x,y,z,θ)

Low level

High level

Xsens IMU

100Hz

~1Hz

78

PDR Filter

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

• Input : Foot mounted IMU measurements (a,ω) (100Hz)• Output : Step events (l,δθ,δz)

• Inertial measurements are integrated using the standard strapdown navigation equations• Zero velocity updates used to reduce drift from cubic-in-time to linear-in-distance-

travelled

• See “Pedestrian Tracking with Shoe-Mounted Inertial Sensors” [E. Foxlin] for more details

79

Representing Building Constraints – 2.5D Maps

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

• Optimised for primary operation: Does step (l,δθ,δz) from (x,y,z,θ) intersect a wall?• Independent of map size (no global spatial search required)• Intersections computed in 2D rather than 3D

• A collection of planar floor polygons• Each polygon has a vertical position, but no depth• Each edge is either a wall or a connection to an edge of another polygon

• A wall is impassable• Connected edges may be separated in the vertical direction (to represent stairs)

80

Localisation Particle Filter

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

• Particle filters describe the state of a system (in this case a pedestrian) as a set of weighted samples (particles) :

St = (st,wt) i = 1…n

• Particle state : st = (xt,yt,θt,polyt)

• A new state is generated each time a step event (l,δθ,δz) arrives from the lower filter

• A new particle (st,wt) is generated from the previous state St-1 as follows:1. Re-sampling Draw a state st-1 from St-1 according to the particle weights

2. Propagation Propagate the sampled state to generate a new state 3. Correction Weight the new particle according to any

measurements

• KLD-sampling used to dynamically vary the number of particles generated at each update

81

Localisation Particle Filter

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

• Propagation :1. Perturb step heading and length by noise

(l’,δθ’) = (l + X, δθ + Y)2. Compute new heading and position

θt = θt-1 + δθ’xt = xt-1 + l’cosθt

yt = yt-1 + l’sinθt

3. Update floor polygon• Test for intersections between the step vector and the edges of polyt-1

• Intersection tests preformed in 2D• Operation speed is independent of map size - no global lookups

• Correction :• If step intersects wall : wt = 0

• Else : Use δz (from the step event) as a measurement• Height change according to map : δzpoly = Height(polyt) - Height(polyt-1)• Set particle weight : wt = N0,σh(| δz - δzpoly|)

• Allows localisation to occur quickly when the user climbs or descends stairs

82

Typical WiFi Localization Using Particle Filter

• 3 Stage– Re-sampling (Initial) stage– Sampling & Random stage– Weighting stage

82

83

Initial Stage

83

5%

5%

5%

5%5%

5%

5%

5% 5%

5%

5%

5%

5%5%

5%

5%

5%

5%

5%

5%

8484

Sampling Stage

5%

5%

5%

5%

5% 5%

5%

5%

5%

5%

5%5%

5%

5%

5%

5%

5%

5%

5%

5%

8585

Random Stage

85

8686

Weighting Stage

Location Server

0.15

5%*0.15

0.08

5%*0.08

0.02

5%*0.02

Pattern MatchingEstimation

8787

Weighting Stage

6%

8%

10% 13%

11% 8% 6% 3% 6%

5% 5% 4%

2% 2%

1%

3%

4%

1%

1%

1%

Particle FilterEstimation

8888

Re-Sampling Stage (1)

6%

8%

10% 13%

11% 8% 6% 3% 6%

5% 5% 4%

2% 2%

1%

3%

4%

1%

1%

1%

Filter out min weight

Samples Space

8989

Sampling Stage and Random (2)

Samples Space

9090

Weighting Stage (3)

Samples Space 6%

1%

30%

1%

1%

1% 1%

1%

1%

1%

1%

1%

1%

2%

2% 2% 2%

2%

2%

8%

6%

7%

4% 4%

4%

4%

9191

After a period of time

9292

Re-Sampling Stage (1)

Samples Space

Sampling area

9393

Sampling Stage and Random(2)

Samples Space

9494

Sampling Stage and Random(2)

9595

Weighting Stage(3)

12%

11%

12%

33%

17%

9%

7%5%

5%

9696

Pedestrian Localisation for Indoor Environments

UbiComp 2008

9797

Re-Sampling Stage (1)

Samples Space

Sampling area

9898

Sampling Stage and Random(2)

Samples Space

9999

Sampling Stage and Random(2)

Samples Space

HumanMobility

100100

Sampling Stage and Random(2)

Illegal Path

101101

Weighting Stage(3)

Center of mass

102

Demonstration Videos

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

See Two Demo Videos

103

Conclusions and Future Work

UNIVERSITY OFCAMBRIDGE

COMPUTER LABORATORY

Pedestrian Localisation for Indoor EnvironmentsOliver Woodman [ ojw28@cam.ac.uk ] and Rob Harle [ rkh23@cl.cam.ac.uk ]

Conclusions

• We have developed a pedestrian localisation system for indoor environments• Foot mounted IMU used to obtain “step” measurements• Localisation techniques developed for robots extended to handle multiple floors• WiFi used to constrain the initial prior

• Position error <0.73m 95% of the time in a typical office environment• Such a system could enable the deployment of location aware applications in large

buildings, where installation of a high accuracy absolute location system is either too expensive or too impractical

Future Work

• Better use of WiFi :• Use WiFi throughout localisation and tracking, rather than just at the start• Use RSSI values to generate tighter constraints

• Use of magnetometer measurements to constrain the heading• Although overcoming magnetic disturbances will be a big problem