Combination of Localization Techniques for Mobile Sensor Network for Precise Localization

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mobilitytrajetorybasedloalization, RSSIbasedloalizationandomputer visionbased loalization. With thissetof mobile

sensornetwork,wehavethepossibilitytodemonstratevariousloalizationmehanismsandtheireetiveness. Inthispaper,the

resultofomputervisionbasedloalization,sensormobilitytrailbasedloalizationandRSSIbasedloalizationwillbepresented.

Keywords: loalization,mobilesensornetwork,RSSI,dead-rekoning,omputervision

1. Introdution. TheresearhesonMobileSensorNetwork(MSN)havebeenplentyworldwide. ForMSN,

there ould bea lot of valuable appliation with attahed sensors as well asapabilities suh as loomotion,

environmentalinformation sensing,dead-rekoning,andsoon. Forsuhappliations, usualrequirementshave

been aknowledged with loalization of eah sensor node and formationof the whole sensor network. Inthis

researh we are going to disuss about loalization tehniques for Mobile Sensor Vehile (MSV) whih an

ompose MSN. In addition, wewill disuss aonstrution of MSN aswellas requiredfuntionalities of eah

MSN.Forthepreiseloalizationwemayanalyzehumanationsforloalization.Forlongdistaneandhugearea

loalization,humansallto theirounterpartand identify ounterpart'sloationby talkingeahother. From

theonversation,onlyaroughloationanbeidentied. Thusweguesslongdistane loalizationonlyallows

rough,inaurate information of loationbut itis suientto onne aregion for morepreise loalization.

For medium distane and medium arealoalization, humanmovesby transportationmethods but eventually

walksin ordertoloalize. Whilewalking,humans buildaoneptualmap fortheloalgeographiinformation

ortheyalready haveknowledgearoundthearea, i.e. theyalreadybuiltmaps. This sortof medium distane

loalization requires relatively morepreiseloalization information than longdistaneloalizationaspreise

as,at least,forwalking,i. e. autonomousdriving.

Forshortdistane andsmallarealoalization,humansdetetounterpartsbyuseofvisualoraural

infor-mation, i. e. they ndtheir friends bytheir eyes. This sort of loalization dedues preise information than

othertwosortofloalization.

Thus, wean onlude and mimi the humanloalization with mobile sensor networks. Even though it

depends on tehniques and environments of the usage of MSN, we anategorize the loalization tehnique

aordingtoitsareaor distane.

There have been variety forms of Mobile Sensor Nodes whih utilizes various tehniques of loalizations

suh asRSSI,GPS, Raider,Laser,Camera,andsoon [1℄[2℄. Oneofthemostprominentone,anRSSIbased

loalization,usuallymeasuresradio signalstrengthanditworks wellwithpopularnetworkdevies. Moreover,

an802.11deviebasedsoftwareapproahanberealizeeasilyaswedidinthispaper. However,RSSImethod

ispronetobefragilewithapreseneofobstales orsowhih willdiminishorattenuateradio signalstrength.

Inashort distane, RSSI signalsusuallyis toohigh that nullify aurateloalization thus it is good forlong

distane,lowaurateloalization.

BymimikinghumanationsforloalizationweanhooseRSSIformobilesensorswhilewirelesstelephones

forhumanandtrajetorybasedtraking,soalledINS(InertialNautialSystem),ashumanwalking. Ofourse,

mapbuildingtehniquesarerequiredforMSVaswellashumans. Forhumanvisualloalization,weanhoose

∗

Thisworkwassupportedbythe2008HongikUniversityResearhFund.

†

Department ofComputer Engineering, Hongik University,Seoul, Korea (hayoonwow.hongik.a.kr ). Thispaper had been

ompletedduringauthor'ssabbatialyearof2009atInstituteofComputerTehnology,ViennaUniversityofTehnology,A-1040

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Fig.1.1. Categorizationof LoalizationTehniques

Fig.1.2.RelativeTehniquesforLoalization

aloalizationtehniquebasedonomputervision. Thusweonludeandategorizeloalizationtehniquesin

threebigategoriesasshowningure1.1sothat weanhooseproperloalization mehanismsforitsusage.

And in order to fulll the loalization, notonly loalization tehniques but also tehniques of ognition,

motionontrol,andpereptionaretightlyrelatedasshowningure1.2. Wemustexpressideaofthispaperin

termsoftheseoneptsof ognition,motionontrol,pereptionandloalization.

Thispaperisorganizedasfollows. Insetion2wewilldisussloalizationmethodthathavebeenresearhed.

Thefollowingsetion3wewillanalyzetherequirementforMSV,thehardwaredesignofMSV,andequipments

forloalization,and wewill disusssoftwareapabilities ofMSV softwareandwill showsoftwareomponents

to fully ontrol our MSV inluding software for MSN itself, monitoringprogram, map building features, and

otherrelatedtopis. Thensetion4willshowstheapproahesofomputervisionbasedloalizationfor small

area, short distane preise loalization. Insetion 5, our approah and methodologyfor mobility trajetory

basedloalizationformediumdistaneloalizationwillbedisussed. InSetion6RSSI(RadioSignalStrength

Identiation)basedloalizationwillbepresentedbasedon802.11devieswithsoftwaremodiation. Finally

setion7will onludethispaperwithpossiblefuture researhtopis.

2. Related Works. There have been a lot of researhes regarding mobile sensor loalization. In this

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trajetory-2.1. RSSI based Loalization. RadioSignal StrengthIdentiation is one of theknownsolutions for

distanemeasure. Itrequireswirelessnetworkdevieformobile sensorsandextrafeatures.

Weuse802. 11networkdevies whihhavewidepopularity. Inadditionweneedommuniationbetween

mobile sensors thus 802.11 networking devies are popular solutions for us. RSSI features for 802.11devie

networks are requiredfeatures in order to implement physial layer of CSMA/CA networking [14℄. However

RSSIbaseddistane measureisverypronetoradio signalattenuationandthus haslowauray. Andithas

somerestrition that one it is a data transfermodes, it annot swithed to API mode instantly. It implies

the restritedrealtimeness for RSSI basedloalization. Manufaturesof 802.11devies usually provide their

arbitrarymethod for RSSI[11℄. In thispaper,wewill demonstrateourMSV suessfully doeslongdistane,

lowaurateloalizationonlywithommerial802.11deviesandnetworkingsoftwareembeddedonMSVs.

2.2. Computer Vision based Approah. There are very few researhes on loalizations by use of

omputer vision tehnology. There have been the previous results regarding mobile sensor vehile ontrol,

obstaledetetionandsoon.

Matsummotoetal.[15℄usedmultipleamerasinordertoontrolmobilerobots. Intheirresearh,ameras

areinstalledontheirworkingspaeinsteadofmobile vehileitself. Theirwhole systemis onsistedofmobile

robots and multiple amerasand this helps thesearh of properpath of robots. Keyes et al. [16℄ researhed

various ameraoptionssuhas lens type, ameratype, ameraloations andsoon. They alsoused multiple

amerastoobtainmorepreiseinformation.

In this paper we will provide MSV with multiple ameras in order to aomplish short distane, high

aurateloalization. However,asingleMSVannotloateitsloationpreisely. Theultimateloalizationan

onlybedonewiththeooperationofnodesinMSN.

Therstrequirementforloalizationisto identifytheloationofolleagueMSVasabasepoint. Forthis

purpose, we preparedthree failities for eah MSV. Eah MSV an estimate its loation by trajetory trail.

Moreover,eahMSVanidentifyotherolleagueMSVwiththeirinfraredLEDsignal. Inaddition,thisloation

informationanbeommuniatedbywirelessnetworkdevie equippedwitheahMSV.

Ofourse,aameraorasetofamerasareinstalledonanMSVinordertoidentifyolleagueMSVs. This

setofamerashasinfraredltersinorderto diminishtheeet ofextralightnoiseinoperatingenvironment.

2.2.1. Loation Determination Problem. With aset of amera, the required information for

loal-ization is olleted from the view of ameras. For example, an infrared LED light an be a parameter to

alulate the olleague's loation. In this researh, weapplied two previousresults. The rst oneis Sample

Consensus(RANSAC)Method [5℄and theseond oneisPnP Method[6℄[7℄.

ForRANSAC method, beauseof leastsquare method, thereis nopossibilityof wrongomputation with

grosserrorvalue. This is themajorreason why weapplied RANSACmethod. Inorder to solvetheproblem

ofonverting3-dimensionalviewto 2-dimensionalameraimage, whihhaslost distaneproblem, weapplied

perspetive-3-point(P3P) problem.

Figure 2.1 shows the basi priniple of P3P problem. The gray triangle is omposed by infrared LED

installed on eah MSV. Points

A, B, C

stand for eah infrared LEDs and these verties ompose a triangle.

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Fig.3.1. DrivingMehanismOutlook

mathematialequationasshownin equation2.1. Theequation2.1is inlosedform. Thenumberofsolutions

fromthese equationswill beuptoeight. However,thereareuptofourpositiveroots.

R

ab

2

=

a

2

+

b

2

−

2 ab

· cos

θ

ab

R

ac

2

=

a

2

+

c

2

−

2 ac

· cos

θ

ac

(2.1)

R

bc

2

=

b

2

+

c

2

−

2 bc

· cos

θ

bc

With this P3Pbasedmethod, weanonlymeasure distanesbetweenobserverandobserved. Forpreise

loalization,wemust identify angle of MSVs aswell. OurMSV is equipped withdigital ompassin order to

identify the angle of MSV based on magneti poles. As predited, digital ompass also has its own errorin

anglemeasurementbutistolerable.

2.3. Autonomous Driving Robot and Dead-Rekoning. This sortof loalization is usually due to

military area. For exampleDARPA, USA invests onunmanned vehile,and their aimis about 30%of army

vehilewithout human on board ontroller. Stanley by Stanford university [17℄, whih earned rst prize in

ompetitions, areequippedwithGPS, 6DOFgyrosopeandanalulatethespeedofdrivingwheels. Those

sensors information anbe ombinedto loatethe position of their unmanned vehile. Theyused omputer

visionsystemwithstereoameraandsingleamera,andlaserdistanemeter,radarinordertogetenvironmental

information. SandstormfromCMU[18℄isequippedwithlaserdistanemeterasamajorsensor. Topographial

modelanbeobtainedbylaserlinesandthespeedofaranbealulatedbythedensityoflaserline. Gimbal

ontheirvehileaninstalllongdistanelasersanner withsevenlasersensors. Shoulder-mountedsensorsan

alulate height informationof topography. Twosannerson bumpersanobtainobstaleinformation. Long

distaneobstales anbeidentiedbyradar.

Our MSV are equipped with RSSI devies, stereo ameras and other sensors for dead-rekoning. Apart

fromtheexamplesof loomotiverobots,theseequipmentsareforaurateloalization.

3. Mobile Sensor Vehile. Wedeveloped MSVin order to experimentourloalization method in real

environment. VariousversionsofMSVaredesignedandimplemented. Theloalizationfuntionsimplemented

onMSVareasfollows:

•

LongdistanelowaurayloalizationbyRSSI

•

Medium distanemediumaurayloalizationbydead-rekoningtraking

•

ShortdistanehighaurayloalizationbyStereoamerawithomputervision.

InthefollowingsubsetionwewilldisusshardwareandsoftwareofMSVrespetively.

3.1. Hardware. MSV is atually a mobile sensor node for MSN. Eah MSV an move autonomously

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Fig.3.2. ObstaleDetetionMehanism

Fig.3.3.StereoEyeSystem

is aterpillar omposed of three wheels, L-type rubber belt, gears as shown in gure 3.1. The adoption of

aterpillarisforminimizationofdrivingerrors. Therearealotofroomstoinstalladditionalsensorhardware.

With digitalompassequippedon thetopofMSV, theauratevehileloationanbesensed. This angular

informationanhelpexatloalizationofMSVs. ThedesignoneptsofMSVareasfollows.

•

Autonomousmobility

•

Extensibilityofequippedsensors

•

Preisemovementandmobilitytrail

AndMSV harateristisasanodeofmobilesensor network areasfollows:

•

Selfidentiation andolleagueidentiation withvariousmethods

•

Wirelessommuniation

•

DigitalCompass

Forautonomousdriving,MSVmustidentifyobstalesandavoidthem. Weuseaninfraredlaserandameras

withinfraredlter. IRlaserisonstantlylightinginparalleltoround. Cameralooksdowngroundsinadegree

of 30 whih is determined by experiments. The onept of this obstale detetion is depited in gure 3.2.

ObstalereetsIRlaserandsensedbyamera[5,7℄. TheobstaleswithreetedIRwillbedetetedaswhite

lines. Thisobstaledetetionwillbeusedbyloal mapbuildingasshownin setion5.1.

Forshortdistaneobstaleswithinthedeadangleofamera,ultrasonisensorsareloatedundertheMSV

andin frontofMSV. For omputervisionbasedloalization,MSVsareequippedwithstereoeyesasshownin

gure3.3. Threeservomotorsanontroltwoamerasindependently. Thisstereoamerasystemanbeused

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Fig.3.4. HardwareStruture

Fig.3.5.SoftwareStruture

For hardwareonstrution, we needmiroontrollerunit, aserial ommuniationport, aPWM port, an

interrupt port in order to ontrol motors and ommuniate with sensors. Figure 3.4 shows the oneptual

strutureofMSVhardware.

3.2. Software. SoftwareforMSVoperationsisrequiredinaformofembeddedsoftware. Figure3.5shows

requiredfailityandtheirstrutureforMSVsoftware.

Totalpartofsoftwareanbedividedinto veategories. Oneoftherolesofsoftwareisto onvertsensor

information into drivinginformation. Information for drivingan be obtainedviaserial ommuniationfrom

T-board(MCU) withdrivinginformationandangularinformation.

The loation of MSV is onstantly updatedwith themoving distane and updated angle. Camera lass

providesobstaleinformation aswellasbasiinformationfor mapbuilding. Mapbuilding lassbuilds amap

withtheinformationfromT-boardlassandameralass. Thesemapsarerequiredforautonomousloomotion

andloalization. NetworklassprovidesnetworkingfuntionalitiesbetweenMSVs.

Weimplementoresoftwarebasedonmultithreads. Thereisdoumentlass, whihprovidesorganidata

owbetweenlasses. Thusthemajorroleof MSVsoftwareisasfollows.

•

Autonomousdriving

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Fig.4.1. CorrelationofLEDpatternintervalandmeasurableminimumdistane

ameraview,driving information,map built,andothersensorinformation. Italso providesmanualoperation

funtionalityofMSV.

4. Computer Visionbased AurateLoalization.

4.1. SensorEquipmentandExperimentalEnvironment. EahMSVhasasetofInfraredLED

(IR-LED)in aform oftriangleandthelengthsofedgesareall30entimeters. TheIR lightsfromtheseLEDsan

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Table4.1

◦

940nm

ThestereoamerasareequippedwithIRlters. ThefrontviewofMSVfor theseequipmentsisasshown

ingure4.2. ThreeIR-LEDsforms atriangleandastereoamerasystemarealsopresented.

Withxedlengthoftriangleedges,i.e. intervalbetweenIR-LED,isxedby30entimeters. Thereforeby

usingP3Pmethod,thedistanebetweenameraandMSVswithIR-LEDtriangleanbealulated. Embedded

software foreah MSV hasarealtime partfor P3Psolution. The softwarealso shows theimage from stereo

ameraasapartofP3Psolution.

Theidealsituationstartsbyestimatingtheanglebetweentwoameras. Oneameradiretionisxed,we

anestimateanglesbetweentrakedobjetandameras,however,MSVanmoveeverydiretionwhihauses

diulties to measure that angle. Moreover,if these amerashavepan-tilt funtionalities, itis impossible to

measuresuhanangleinrealtime.

Anothermethod withP3P tehniqueis toassume thedistaneto theobjet. Thedistaneto objetand

the saleof triangle in ameraviewis proportionalinversely thus thesize of LED triangle anbe astarting

point to estimate the distane to obstales. Wedeided to standardizethe redued sale of LEDtriangle in

order to estimate distane to objets. Thebasionept of this method is depited in gure 4.1 andwill be

disussedfurtherin thesubsetion4.3.

Thisapproahhaslimitsofameravisibility,i.e. objetsbeyondvisibilityannotbeidentied. However,

two other loalization methods will be presented in the following setions for beyond sight loalization. In

additionwiththehelpofdigitalompass,weanmeasurethediretionofeahMSV.Theombinationofthis

informationanahieveshort distaneaurayforloalization.

4.2. Preliminary Experiment for Equipment Setup. We ondut preliminary experimentin order

to hoose optimal devie for omputervision based loalization. The rst purpose of this experiment is to

selet thebest LED in order to inreasethe range of loalization. Our pastresultshowed 250entimeter of

loalizationrangehoweverouraimistoenlargetherangeto400entimetersorfarther.

We hoose ve infrared light emitting diodes with typial harateristis. We rst onentrated on the

visible angle of LEDlights sineweassumed widervisible angle guaranteesthe leareridentiation of LED

lightand morepreiseloalization.

Table4.1 showsthespeiationsofvariousIR-LEDs withvisibleangle andpeakwavelength. Themajor

reasonwhywehoosethoseIR-LEDsareasfollows:

•

Smallerhalf angleof LEDenableslongdistanetrakinghoweverinreasesinvisibilityfrom theside.

•

LargerhalfangleofLEDenablestrakingfromthesidehoweverdereasestrakingdistane.

WithinfraredlterequippedamerasweplannedexperimentstoevaluatetheLEDsforvisionbased

loal-ization. Table4.2 showtheresultofvisibledistaneand visibilityofIR-LEDs. Twelveexperimentshavebeen

madeandaveragevaluesareshown. FromthespeiationsofIR-LEDs,5voltsDCvoltageissuppliedforthe

experiment.

Among ve IR-LEDs, twoshowed stable visibility and aeptable visibility distane. Betweenthese two

andidates,wenallyhoose thebest LEDofMODEL NO.SI5313-Hsineithasthewidesthalf angleaswell

withreasonablevisibilitydistane.

4.3. MainExperimentsforComputerVisionbasedLoalization. Figure4.1showstherelationship

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tan

θ

=

2 h

(4.1)

θ

= arctan

d

2 h

Mostofamerashasangleofviewin

54

◦

_∼

60

◦

. Sine weusedamerawithangleofviewin

60

◦

, fromthe

equation4.1weansolveratioabout

h

:

d

= 1 : 1

.

08

. Theatualvalueof

d

is30entimeterforourexperiment.

Thus weansummarizethefollowing:

•

Highangleofviewameraaninreaseminimummeasuredistane.

•

WithnarrowLEDpatterninterval,weandereaseatualdistane

h

butpratiallymeaningless.

•

WithwiderLEDpatterninterval,weaninreaseatualdistanebut dependentonMSVsize.

From theexperiments, weanidentify thevision basedloalization iseetivewithin the rangefrom 30

entimeters to 520 entimeters with our LogiTeh CAM amera. The 30 entimeter lower bound is due to

the30 entimeter intervalof LEDtriangleedges. The520entimeter upper bound is due tothe visiblesight

apabilityof LogiTeh CAMamera. Thus 520entimeter would beamaximumdistane of omputervision

basedloalization. Howeveritis still meaningfulsineweanahieveveryhigh aurayin loalization with

these heap, low grade ameras. The other idea formore loalization distane is to use ameraswith higher

resolution.

Fromourexperiments,weidentiedtheorrelationbetweenatualdistanefromameratoolleagueMSV

and size of LED triangles in amera view. The results an be translated into graphial form as shown in

gure4.3.

From gure 4.3 the result shows the utuation of results with morethan 500entimeters whih makes

loalizationunstable. Forappliationswhihrequiretheerrorrangeof 20entimeters,weanusetheresults

to520entimeters. Sineouraimisto keeploalizationerrorswithin therangeof 10entimeters, wedeided

todisardresultsmorethan400entimeters.

4.4. Experimental Result. Fromour experimentin theprevious subsetions we will provide thenal

resultofomputervisionbasedloalizationinthissubsetion. Figure4.4showsgraphialversionofnalresult.

Apartfrom theresultsin previoussetion, thisgureshowsatualdistane upto 500entimeters. From

gure4.3weanobserveerrorsinalulatedvaluesofP3Pformorethan500entimeterdistane. Theseerrors

isdueto theresolutionlimitofCAMamerawhih is

640 ×

480

. Even asmall noiseanvaryatual distane

oftenentimetersinthedistanemorethan500entimeters.

Thus weonludetheaurateloalizationbyomputervisionan bedonein therangeof70entimeters

to500entimeterswithourameraequipments. Fortheloalizationinmorethan500entimeters,loalization

basedon MSV trajetorytrakingwill beeetive. In addition, for theloalization in morethan30 meters,

loalizationbased onRSSIwill beeetive6. Of ourse, theloationinformationanbebroadasted andbe

usedbythemembersofMSNinordertobuild maps,toorretloationerrorsand soon.

5. Mobility Trail based Loalization.

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Fig.5.1.RelativeCoordinate

Fig.5.2. AbsoluteCoordinate

transferred to global map. MSVs ommuniate with eah other in order to ombine loal maps into global

informationidentiedbyMSVisinaformofrelativeoordinate. Inrelativeoordinates,theveryfrontofMSV

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Fig.5.3. ResultofUMBmark

Table5.1

COMPARISONBETWEENGRIDMAPANDTOPOLOGICALMAP

MAPS Grid MAP TopologialMAP

Advs.

Preise presentationof geographyof

environment

Ease of algorithm design:

environ-mentalmodeling,pathnding,

loal-izationbymap-mathing

Simple presentation of environment

andsimplepathplanning

Tolerane of low auray mobile

sensors

Naturalinterfaeto users

Disadvs.

Diultyin pathplanning

Requirement of large memory and

omputation

Poor interfae to symboli problem

solver

Impossibility of large map building

withinaurate,partialinformation

Diulties in map-mathing:

di-ultiesin alulation ofpivot sensor

value

Diulties in dealing omplex

envi-ronment

Loalmap isusually inaform ofgridmap. Howeverinaseofglobalmapwithhugeapaities, gridmap

isveryineient. ThereforewewillusetopologialmapforglobalmapaspresentedbyKuipersandBynn[6℄.

Thrun[8℄presentedahybridapproah ofbothmaps and wewill onsider itasourultimate formatof global

map. Table5.1omparesadvantagesanddisadvantagesofgridandtopologialmap.

With mobility trajetory traking, medium range loalization an be implemented by use of loal map.

EahMSVmovesautonomouslyandbuilditsownloalmap. Inthefollowingsubsetion,wewilldisusserror

orretionsofmobilitytrakingbasedapproahwhihisessentialtoguaranteetheaurayofloalization.

5.2. Dead-Rekoning. For the medium distane loalization,we deided to utilize mobility trail. We

dene the range of medium distane between 4 meters and 40 meters sine our vision based short distane

loalization overswithin therange of5 meters andRSSI basedlongdistane loalization is eetiveoutside

therangeof30meters. OuraimistotrailthemobilityofMSV andtoreordthetrailontheloalmap with

reasonable auray for medium distane loalization. Every driving mehanism for mobile sensors or even

mobile robots hasmehanialerrors andit is impossible to avoidsuh errorspratially. Wean summarize

theauseof drivingerrorsasfollowings:

•

Thedierenebetweenthesizesoftwo(leftandright)wheels

•

Thedistortionofwheelradius,i. e. thedistanebetweenaverageradiusandnominalradius

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•

Theunertaintyabouttheeetivewheelbase

•

Therestritedresolutionofdrivingmotors(usuallystepmotors)

Usually those errors are umulated and nal result will be void without proper error orretion tehnique.

However,inordertoopewiththoseloationerrorsduetomehanialerrors,amethodofdead-rekoninghave

beenwidelyused and wealso adopt suh tehniqueaswell. Dead-rekoningisamethodology that alulates

themovingdistaneoftwowheels ofMSVandderivestherelativeloationfromtheoriginofMSV.

Among thevarious versions of Dead-rekoningtehniques, we used UMBmark tehnique from University

of Mihigan [4℄. UMBmark analyzes driving mehanism errors and minimized the eet of driving errors.

UMBmark analyzes the result of MSV driving in a ertain distane and ompensates mehanial errors of

MSV driving mehanism. The driving results of retangular ourse, both in lokwise(CW) and

ounter-lokwise(CCW),andthenanalyzed.

TwoerrorharateristisarelassiedinRotationangleerrorandWheelmismatherror. Rotationalangle

errorsareforthedierenebetweenatualwheelsizesandtheoretialdesignsizesofwheels. Duetorotational

angleerrors,CCWdrivingafterCWdrivingshowslargererrorsasusual. Forexample,atualwheelsizebigger

thandesignedwheelsizeresultsininsuientrotationatornersandthenrotationalangleerrorsareumulated

forthewholedriving. Thefollowingequationsummarizestherotationalangleerrorwhihisdepitedin [4℄.

E

d

=

D

R

D

L

where

D

R

isdiameterofleftwheeland

D

R

isdiameterofrightwheel. Inshort,

E

d

isarationbetweendiameters

ofleftwheelandrightwheel.

Wheel mismath errors are from wheelbasemismath. This error ausesskews in straightdriving. With

wheelmismatherror,theerrorharateristiofCWdrivingisoppositetoCCWdriving. Thefollowingequation

summarizesthewheelsizeerrorwhih isdepitedin [4℄.

E

b

=

90

◦

90

◦

₋

_α

where

α

isavalueofrotationalangleerror.

E

b

standsforarationbetweenidealandpratialerrorsinrotation,

i.e. wheelbaseerror.

Mehanialerrorsaresystematialerrorsandthereforeanbepreditedandanalyzed,whilenon-mehanial

errorsannotbepreditedbeausenon-mehanialerrorsareduetothedrivingenvironment. Non-mehanial

errorsarelassiedasfollows:

•

Unevendriving oororground

•

Unpreditedobstaleondrivingourse

•

Slippingwhiledriving

WeappliedUMBmarktoourMSVandthefollowingsubsetionshowstheresult.

5.3. Driving Error Corretion of MSV. Weomposed aset of experiment forMSVdriving in order

toapplyUMBmark. Thedrivingexperimentshavebeenmadeontheatandusualoorwiththeretangular

drivingourseof4

×

4meters. Asshownin [4℄bothCWand CCWdrivinghavebeenmadeand errorvalues

havebeenmeasured. Theseerrorvaluesareinorporatedin oursoftwaresystemandMPUontrollers.

With thefollowingequationsfrom [4℄weanndtheerrorvalueforerrororretion.

b

actual

=

E

b

×

b

nominal

where

b

actual

isanatualwheelbaseand

b

nominal

isameasuredwheelbase.

∆

U

L,R

=

c

L,R

×

c

m

×

N

L,R

WhereUisatualdrivingdistane,Nisthenumberofpulsesoftheenoder,and

c

m

istheoeienttoonvert

pulseperentimeters.

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Fig.5.4. TriangulationWithRSSIMeasurement

Fig.5.5. RSSIvaluesAordingToDistane

dotes are of unorreted driving results while lleddotes are of driving results with UMBmark in 16 meter

drivingexperiment. NotethattheoriginofMSV(startingpoint)attheoordinate(0,0)areattheupperright

partof thegure. Withoutdead-rekoningtehnology,MSVreturnsto erroneouspointthantheoriginpoint,

at the left partof thegure. This MSV tends to show moreerrors with CW driving. With theappliation

of UMBmarktehnique, weahievedfaithful resultwithin 10 entimeters of errorrange in total. Diretional

errorsarewithintherangeof3entimetersfromtheorigin. Sineourapproahisformehanialdrivingerrors,

non-mehanialerrorsanbeavoidedandthuswewillintroduereal-timeorretionofdrivingwiththehelpof

digitalompassforthefutureresearhes. ThusitispossibletomentionthatthetrailofMSV isintheorret

loationwithintheerrorsof10min ourexperimental environments.

6. RSSIbasedLong Distane andWide Area Loalization. OurMSVareequippedwith

homoge-neous802.11networkingdevies withRSSIfailities. With distane informationweandotriangulationwith

at leastthree nodes and oneanhor. Ourmonitoringstation withmonitoringprogram anatasan anhor.

The802.11networkingdevies anbeswithed toAP (Aess Point)modeso that eah MSNanat asAP.

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Fig.5.6.RelationBetweenOneFixedAnhorAndMobileSensorVehiles

TheunitofRSSIisindBm(-50dBm

∼

-100dBm)anditdesignatesdistanesbetweenspeiedMSVs. As

already mentioned, the RSSI value is veryaetive by environments, and we obtain errorrate of 10

∼

15%

in spei distane. The RSSIvalue isverysensitivewith hardware vendorand thediretion of AP [9℄. Our

experimentalenvironmentisasfollows.

•

Wideopenarea

•

IntelWirelessLAN21003BMiniPCAdapter

•

WRAPIsoftwaremodel[11℄

•

Onexedanhoras monitoringstation

Forthealibrationof ourRSSIdevie, asetof experimenthasbeenonduted andtheresultsare shown

ingure5.5.

Weanndwithin thedistaneof 20meter,RSSIisnomoreusefulfordistane measuresinethesignal

strength is too high. Ourexperiments showsRSSI basedloalizationis useful morethan 35mdistane. The

values are within error range of 15% by experiment. This is the main reason why we hoose RSSI based

loalizationforlongdistane, lowaurayloalization.

FromthedistaneinformationfromRSSIsensing,weandotriangulationasshowningure5.4. Foratual

implementation,wehaveonexedanhorandandomorepreiseloalizationwithaknownanhoroordinate

asshowningure5.6. Theguresshowthreemobilenodesoneanhornode. Thedistaneobtainedfromirle

r

1 , r

2 , r

3

an be obtainedfrom RSSI values. Thus with this environment we an triangulatethe oordinate

node

X

fromtheintersetionofirlesdrawnbynode1,node2,andnode3[12℄[13℄.

Thus from the distane whih an be obtained from RSSI values, let the distane be d

i

from radius of

irler

i

Thefollowingalgorithm2showsaproedureto ndoordinatesofeahMSV withprovided distane

informationbyRSSI.

Figure 6.1 showsanal result in RSSI basedloalization. The x-axisstandsfor atualdistane between

MSVs andy-axisshows adistane alulatedbyalgorithm 2. As wepredited theRSSI basedloalization is

usefulwiththedistanemorethan30meters. OntherangewhereRSSIbasedloalizationiseetive,wean

see errors between atual distane and alulated distane. We believe it is tolerablesine we have another

methodofloalizationwithmoreauraywithinthedistaneof30meters.Ofourse,thedistaneinformation

is not a suientondition for loalization. The other information of diretion of MSV an beobtained by

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distane between (x1,y1) and (x2,y2))

{

for(eah (x3,y3) on Cirle r3)

{

if (d2 ==

distane between (x2,y2) and (x3,x3))

{

if (d3 ==

distane between (x3,y3) and (x1,y1))

{

SolutionList =

Coordinate (x1,y1),(x2,y2),(x3,y3)

}}}}}}

Fig.6.1. AtualandRSSIbasedDistane

7. Conlusions. Oftheloalizationmethodologiesformobilesensornetwork,weombinedthreedierent

ategoriesofloalizationmethodology. Inadditionfortheexperiment,weimplementedmobilesensorvehileas

anodeofmobilesensornetwork. Weshowedbriefdesriptionofourmobilesensorvehileinludinghardware

andsoftwarefuntionalities. Aomputervisionbasedapproahhasbeenpresentedforthesmallarealoalization

withaonsiderable rangeof preiseness. The drivingmehanismhardware andsoftware ooperatewith eah

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maps and eventually be merger into global maps. In additionwe showed RSSI based loalization. The long

distane, low auray loalization an be implemented by ommerial 802.11 networking devies only with

softwarebutwithoutanyotherspeihardwaredevie..

Fromthesethreelevelsofloalization,webelievethatweimplementedusefulloalizationsystemandwilldo

moreresearhusingthisplatform. ForexamplemultipleMSVanooperateandommuniateeahotherand

thenaformationbasedonloalizationanbemade. Asmoothtransitionbetweentheseloalizationinformation

forspei environmentorappliationwithprobabilitymodelisournextgoaltoahieve.

Aknowledgments. Theauthor givesspeialthankstothepasto-authorsinludingMr. KwansikCho,

Mr. Jaeyoung Park and many other olleagues. Theirontributions and allowanes lead to this paper with

unitedresults.

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Editedby: JanuszZalewski

Reeived: September30, 2009