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ProbabilisticRoboticsSample-basedLocalization(sonar)Representbeliefbyrandomsamples
Estimationofnon-Gaussian,nonlinearprocesses
MonteCarlofilter,Survivalofthefittest,Condensation,Bootstrapfilter,Particlefilter
Filtering:[Rubin,88],[Gordonetal.,93],[Kitagawa96]
Computervision:[IsardandBlake96,98]
DynamicBayesianNetworks:[Kanazawaetal.,95]dWeightsamples:w=f/gImportanceSamplingwithResampling:LandmarkDetectionExampleDistributionsDistributionsThisisEasy!ImportanceSamplingwithResamplingImportanceSamplingwithResamplingParticleFiltersSensorInformation:ImportanceSampling
SensorInformation:ImportanceSamplingRobotMotionAlgorithmparticle_filter(St-1,ut-1zt):
ForGeneratenewsamples
Sampleindexj(i)fromthediscretedistributiongivenbywt-1
Samplefromusingand
Computeimportanceweight
Updatenormalizationfactor
Insert
For
Normalizeweights
drawxit-1fromBel(xt-1)Resamplingw2Algorithmsystematic_resampling(S,n):
For Generatecdf
Initializethreshold
For Drawsamples…
While() Skipuntilnextthresholdreached
Insert
Incrementthreshold
ReturnS’StartProximitySensorModelReminder23242526272829303132333435363738394041Sample-basedLocalization(sonar)InitialDistributionAfterIncorporatingTenUltrasoundScansAfterIncorporating65UltrasoundScansEstimatedPathUsingCeilingMapsforLocalizationVision-basedLocalizationUnderaLightNexttoaLightElsewhereGlobalLocalizationUsingVisionRobotsinAction:AlbertApplication:RhinoandAlbertSynchronizedinMunichandBonnLocalizationforAIBOrobotsLimitationsApproachesRandomSamplesVision-BasedLocalizationOdometryInformationImageSequenceResultingTrajectoriesResultingTrajectoriesGlobalLocalizationKidnappingtheRobotRecoveryfromFailureSummary
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