基于视觉的矿井救援机器人场景识别毕业论文外文文献翻译及原文

毕业设计（论文）外文文献翻译文献、资料中文题目：基于视觉的矿井救援机器人场景识别文献、资料英文题目：Scenerecognitionforminerescuerobot文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：班级：姓名：学号：指导教师：翻译日期：2017.02.14附录A英文原文ScenerecognitionforminerescuerobotlocalizationbasedonvisionCUIYi-an,CAIZi-xing，WANGLuAbstract：AnewscenerecognitionsystemwaspresentedbasedonfuzzylogicandhiddenMarkovmodel(HMM)thatcanbeappliedinminerescuerobotlocalizationduringemergencies.Thesystemusesmonocularcameratoacquireomni-directionalimagesofthemineenvironmentwheretherobotlocates.Byadoptingcenter-surrounddifferencemethod,thesalientlocalimageregionsareextractedfromtheimagesasnaturallandmarks.TheselandmarksareorganizedbyusingHMMtorepresentthescenewheretherobotis,andfuzzylogicstrategyisusedtomatchthesceneandlandmark.Bythisway,thelocalizationproblem,whichisthescenerecognitionprobleminthesystem,canbeconvertedintotheevaluationproblemofHMM.Thecontributionsoftheseskillsmakethesystemhavetheabilitytodealwithchangesinscale,2Drotationandviewpoint.Theresultsofexperimentsalsoprovethatthesystemhashigherratioofrecognitionandlocalizationinbothstaticanddynamicmineenvironments.Keywords:robotlocation;scenerecognition;salientimage;matchingstrategy;fuzzylogic;hiddenMarkovmodel1IntroductionSearchandrescueindisasterareainthedomainofrobotisaburgeoningandchallengingsubject[1].Minerescuerobotwasdevelopedtoenterminesduringemergenciestolocatepossibleescaperoutesforthosetrappedinsideanddeterminewhetheritissafeforhumantoenterornot.Localizationisafundamentalprobleminthisfield.Localizationmethodsbasedoncameracanbemainlyclassifiedintogeometric,topologicalorhybridones[2].Withitsfeasibilityandeffectiveness,scenerecognitionbecomesoneoftheimportanttechnologiesoftopologicallocalization.Currentlymostscenerecognitionmethodsarebasedonglobalimagefeaturesandhavetwodistinctstages:trainingofflineandmatchingonline.Duringthetrainingstage,robotcollectstheimagesoftheenvironmentwhereitworksandprocessestheimagestoextractglobalfeaturesthatrepresentthescene.Someapproacheswereusedtoanalyzethedata-setofimagedirectlyandsomeprimaryfeatureswerefound,suchasthePCAmethod[3].However,thePCAmethodisnoteffectiveindistinguishingtheclassesoffeatures.Anothertypeofapproachusesappearancefeaturesincludingcolor,textureandedgedensitytorepresenttheimage.Forexample,ZHOUetal[4]usedmultidimensionalhistogramstodescribeglobalappearancefeatures.Thismethodissimplebutsensitivetoscaleandilluminationchanges.Infact,allkindsofglobalimagefeaturesaresufferedfromthechangeofenvironment.LOWE[5]presentedaSIFTmethodthatusessimilarityinvariantdescriptorsformedbycharacteristicscaleandorientationatinterestpointstoobtainthefeatures.Thefeaturesareinvarianttoimagescaling,translation,rotationandpartiallyinvarianttoilluminationchanges.ButSIFTmaygenerate1000ormoreinterestpoints,whichmayslowdowntheprocessordramatically.Duringthematchingstage,nearestneighborstrategy(NN)iswidelyadoptedforitsfacilityandintelligibility[6].Butitcannotcapturethecontributionofindividualfeatureforscenerecognition.Inexperiments,theNNisnotgoodenoughtoexpressthesimilaritybetweentwopatterns.Furthermore,theselectedfeaturescannotrepresentthescenethoroughlyaccordingtothestate-of-artpatternrecognition,whichmakesrecognitionnotreliable[7].Sointhisworkanewrecognitionsystemispresented,whichismorereliableandeffectiveifitisusedinacomplexmineenvironment.Inthissystem,weimprovetheinvariancebyextractingsalientlocalimageregionsaslandmarkstoreplacethewholeimagetodealwithlargechangesinscale,2Drotationandviewpoint.Andthenumberofinterestpointsisreducedeffectively,whichmakestheprocessingeasier.FuzzyrecognitionstrategyisdesignedtorecognizethelandmarksinplaceofNN,whichcanstrengthenthecontributionofindividualfeatureforscenerecognition.Becauseofitspartialinformationresumingability,hiddenMarkovmodelisadoptedtoorganizethoselandmarks,whichcancapturethestructureorrelationshipamongthem.SoscenerecognitioncanbetransformedtotheevaluationproblemofHMM,whichmakesrecognitionrobust.2SalientlocalimageregionsdetectionResearchesonbiologicalvisionsystemindicatethatorganism(likedrosophila)oftenpaysattentiontocertainspecialregionsinthescenefortheirbehavioralrelevanceorlocalimagecueswhileobservingsurroundings[8].Theseregionscanbetakenasnaturallandmarkstoeffectivelyrepresentanddistinguishdifferentenvironments.Inspiredbythose,weusecenter-surrounddifferencemethodtodetectsalientregionsinmulti-scaleimagespaces.Theopponenciesofcolorandtexturearecomputedtocreatethesaliencymap.Follow-up,sub-imagecenteredatthesalientpositioninSistakenasthelandmarkregion.Thesizeofthelandmarkregioncanbedecidedadaptivelyaccordingtothechangesofgradientorientationofthelocalimage[11].Mobilerobotnavigationrequiresthatnaturallandmarksshouldbedetectedstablywhenenvironmentschangetosomeextent.Tovalidatetherepeatabilityonlandmarkdetectionofourapproach,wehavedonesomeexperimentsonthecasesofscale,2Drotationandviewpointchangesetc.Fig.1showsthatthedoorisdetectedforitssaliencywhenviewpointchanges.Moredetailedanalysisandresultsaboutscaleandrotationcanbefoundinourpreviousworks[12].3ScenerecognitionandlocalizationDifferentfr