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1、SimultaneousLocalizationandMapping(SLAM)Lecture01IntroductionSLAMObjective•Placearobotinanunknownlocationinanunknownenvironmentandhavetherobotincrementallybuildamapofthisenvironmentwhilesimultaneouslyusingthismaptocomputevehiclelocation•SLAMbeganwithseminalpaperbyR.Smith,M.Self,andP.Cheese
2、manin1990•AsolutiontoSLAMhasbeenseenasthe“HolyGrail”–Wouldenablerobotstooperateinanenvironmentwithoutaprioriknowledgeofobstaclelocations•Researchoverthelastdecadehasshownthatasolutionispossible!!TheLocalizationProblemDefined•Amapmoflandmarklocationsisknownapriori•Takemeasurementsoflandmark
3、locationz(i.e.distanceandbearing)k•Determinevehiclelocationxbasedonzkk–Needfilterifsensorisnoisy!•xk:locationofvehicleattimek•uk:acontrolvectorappliedatk-1todrivethevehiclefromxk-1toxk•zk:observationofalandmarktakenattimekk•X:historyofstates{x1,x2,x3,…,xk}k•U:historyofcontrolinputs{u1,u2,u
4、3,…,uk}•m:setofalllandmarksTheMappingProblemDefinedk•ThevehiclelocationsXareprovided•Takemeasurementoflandmarklocationz(i.e.distanceandbearing)k•Buildmapmbasedononzk–Needfilterifsensorisnoisy!k•X:historyofstates{x1,x2,x3,…,xk}•zk:observationofalandmarktakenattimek•m:truelocationoftheithlan
5、dmarki•m:setofalllandmarksSimultaneousLocalizationandMappingDefinedk•FromknowledgeofobservationsZk–DeterminevehiclelocationsX–Buildmapmoflandmarklocations•xk:locationofvehicleattimek•uk:acontrolvectorappliedatk-1todrivethevehiclefromxk-1toxk•m:truelocationofithlandmarki•zk:observationofala
6、ndmarktakenattimekk•X:historyofstates{x1,x2,x3,…,xk}k•U:historyofcontrolinputs{u1,u2,u3,…,uk}•m:setofalllandmarksk•Z:historyofallobservations{z1,z2,…,zk}H.Durrant-Whyte,D.Rye,E.Nebot,“LocalisationofAutomaticGuidedVehicles”,ISRR1995SimultaneousLocalizationandMappingCharacteristics•Localizat
7、ionandmappingarecoupledproblems–Twoquantitiesaretobeinferredfromasinglemeasurement•AsolutioncanonlybeobtainedifthelocalizationandmappingprocessesareconsideredtogetherH.Durrant-Whyte,D.Rye,E.Nebot,“LocalisationofAutomaticGuidedVehicles”,RoboticsResearch:The7thI
