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容积卡尔曼滤波在车辆定位中的应用研究.docx

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容积卡尔曼滤波在车辆定位中的应用研究 Abstract摘要 Withtherapiddevelopmentofautonomousdrivingtechnology,accurateandreliablevehiclepositioningiscrucialforensuringthesafetyandefficiencyoftransportationsystems.Inrecentyears,theKalmanfilterhasbeenwidelyusedinvehiclepositioningduetoitseffectivenessinfusingnoisysensormeasurementsandpredictingvehiclestates.Inthispaper,weinvestigatetheapplicationofthevolumetricKalmanfilterinvehiclepositioningandevaluateitsperformancethroughsimulationsandexperiments.TheresultsdemonstratethatthevolumetricKalmanfiltercaneffectivelyimprovetheaccuracyandrobustnessofvehiclepositioning,makingitapromisingapproachforfutureautonomousdrivingsystems. 1.Introduction引言 Vehiclepositioningisafundamentaltaskinautonomousdrivingsystemsasitprovidescriticalinformationaboutthevehicle'slocationandorientation.Traditionalapproachestovehiclepositioningrelyonglobalnavigationsatellitesystems(GNSS),whichsufferfromlimitationsinurbanareaswithtallbuildingsandinenvironmentswithsignalobstructions.Toovercometheselimitations,integratingmultiplesensormeasurements,suchasinertialsensors,wheelodometry,andenvironmentalperceptiondata,hasbecomeapopularsolution. TheKalmanfilterisarecursiveestimationalgorithmthathasbeenwidelyusedinvehiclepositioningduetoitsabilitytofusenoisysensormeasurementsandpredictvehiclestates.However,traditionalKalmanfiltersassumethatthevehicle'sstateisapointestimate,neglectingitsuncertaintyorambiguity.Thiscanleadtoinaccurateestimates,especiallyincomplexurbanenvironments. Toaddressthisissue,thevolumetricKalmanfilterhasbeenproposedasanextensionofthetraditionalKalmanfilter.ThevolumetricKalmanfilterrepresentsthevehicle'sstateasaprobabilitydistributionintheformofaGaussianvolume,ratherthanapointestimate.Byconsideringtheuncertaintyofthevehicle'sstate,thevolumetricKalmanfiltercanprovidemorerobustandaccurateestimatesofthevehicle'spositionandorientation. 2.Methodology方法 ThevolumetricKalmanfilteroperatesinasimilarmannertothetraditionalKalmanfilter,butwithadifferentstaterepresentationandcovari