Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps

Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps

ID:40707791

大小:1.68 MB

页数:8页

时间:2019-08-06

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1、2018IEEEInternationalConferenceonRoboticsandAutomation(ICRA)May21-25,2018,Brisbane,AustraliaAutonomousVehicleNavigationinRuralEnvironmentswithoutDetailedPriorMapsTeddyOrt1,LiamPaull1,2,DanielaRus1Abstract—State-of-the-artautonomousdrivingsystemsrelyheavilyonde

2、tailedandhighlyaccuratepriormaps.However,outsideofsmallurbanareas,itisverychallengingtobuild,store,andtransmitdetailedmapssincethespatialscalesaresolarge.Furthermore,maintainingdetailedmapsoflargeruralareascanbeimpracticableduetotherapidrateatwhichtheseenviron

3、mentscanchange.Thisisasignificantlimitationforthewidespreadapplicabilityofautonomousdrivingtechnology,whichhasthepotentialforanincrediblypositivesocietalimpact.Inthispaper,weaddresstheproblemofautonomousnavigationinruralenvironmentsthroughanovelmaplessdrivingfr

4、ameworkthatcombinessparsetopologicalmapsforglobalnavigationwithasensor-basedperceptionsystemforlocalnavigation.First,alocalnavigationgoalwithinthesensorviewofthevehicleischosenasawaypointleadingtowardstheglobalgoal.Next,thelocalperceptionsystemgeneratesafeasib

5、letrajectoryinthevehicleframetoreachthewaypointwhileabidingbytherulesoftheroadforthesegmentbeingtraversed.Thesetrajectoriesareupdatedtoremaininthelocalframeusingthevehicle’sodometryandtheassociateduncertaintybasedontheleast-squaresresidualandarecursivefiltering

6、approach,whichallowsthevehicletonavigateroadnetworksreliably,andathighspeed,withoutdetailedpriormaps.Wedemonstratetheperformanceofthesystemonafull-scaleautonomousvehiclenavigatinginachallengingruralenvironmentandbenchmarkthesystemonalargeamountofcollecteddata.

7、I.INTRODUCTIONAutonomousdrivinghasthepotentialtodrasticallyim-proveourlives.Todate,thevastmajorityoffieldedau-tonomousvehiclesfocusononeoftwoscenarios:Fig.1:MaplessNavigationusingSparseTopologicalMaps.1)Lanefollowingonwell-markedstructuredhighwaysTop:Thecrowd-s

8、ourcedtopologicalmapfromopenstreetmap.orgisshownasredsegmentsconnectingyellowvertices.Thepoint2)Urbannavigationbasedonextremelypreciseandman-cloudobtainedfromaVelodyneHDL-64lasersc

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