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1、ARobustMethodforComputingVehicleEgo-motionGideonP.SteinOferManoAmnonShashuaMobileEyeLtd.HebrewUniversity,Jerusalemgideon,ofer@mobileye.comshashua@cs.huji.ac.ilAbstractlierinformation.ForexamplemovingtrafficviolatesWedescribearobustmethodforcomputingtheego-motiontheri
2、gidworldassumptionandthuscontributesfalseofthevehiclerelativetotheroadusinginputfromasin-measurementsforego-motioncalculation;alsorainglecameramountednexttotherearviewmirror.Sincedrops,wipermovinginrainconditions,glare,andfeaturepointsareunreliableinclutteredscenesw
3、eusedi-soforth,allcontributefalsemeasurementsforego-rectmethodswhereimagevaluesinthetwoimagesaremotionrecovery.combinedinaglobalprobabilityfunction.CombinedwithToovercometheseproblems,firstandforemost,wepro-theuseofprobabilitydistributionmatrices,thisenablesposeanapp
4、roachbasedonadirectmethod[4,1,11,10]theformulationofarobustmethodthatcanignorelargewhereeachpixelcontributesameasurement.Thesemea-numberofoutliersasonewouldencounterinrealtrafficsurementsarethencombinedinaglobalprobabilityfunc-situations.Themethodhasbeentestedinrealw
5、orlden-tionfortheparametersoftheego-motionmodel.The“di-vironmentsandhasbeenshowntoberobusttoglare,rainrect”approachhastheadvantageofavoidingthecalcu-andmovingobjectsinthescene.lationofoptic-flowandinturnavoidstheuseoffeaturetracking.Asaresult,thecollinearimagestructu
6、resthat1IntroductionareprevalentintypicalroadwayscontributemeasurementsAccurateestimationoftheego-motionofthevehiclerela-fortheego-motionmodel.Second,wereducethenum-tivetotheroadisakeycomponentforautonomousdrivingberofestimatedparameterstoaminimumofthreeparam-andcom
7、putervisionbaseddrivingassistance.Wedescribeeters.Thishastheadvantageofdisambiguatingtypicalarobustmethodforcomputingtheego-motionoftheve-ambiguoussituationsbydecouplingrotationalandtransla-hiclerelativetotheroadusinginputfromasinglecamerationalmotion,andmostimporta
8、ntlyfacilitatestheuseofrigidlymountednexttotherearviewmirror.Themethodrobustestimationusingsampling[7].hasbeentestedinrealworldenvironment