matlab中英文翻译文献

附录英文原文ScenerecognitionforminerescuerobotocalizationbasedonvisionAbstract：AnewscenerecognitionsystemwaspresentedbasedonfuzzylogicandhiddenMarkovmode1(HMM)thatcanbeapp1iedinminerescuerobotlocalizationduringemergencies.Thesystemusesmonocu1arcameratoacquireomni—directionalimagesofthemineenvironmentwheretherobotlocates.Byadoptingcenter-surrounddifferencemethod,thesalientloca1imageregionsareextractedfromtheimagesasnaturallandmarks.TheselandmarksareorganizedbyusingHMMtorepresentthescenewheretherobotis,andfuzzylogicstrategyisusedtomatchthesceneand1andmark.Bythisway,theloca1izationproblem,whichisthescenerecognitionprob1eminthesystem,canbeconvertedintotheeva1uationprob1emofHMM.Thecontributionsoftheseski1lsmakethesystemhavetheabilitytodealwithchangesinscale,2Drotationandviewpoint.Theresultsofexperimentsa1soprovethatthesystemhashigherratioofrecognitionandloca1izationinbothstaticanddynamicmineenvironments.Keywords:robotlocation;scenerecognition；salientimage;matchingstrategy；fuzzy1ogic;hiddenMarkovmode11IntroductionSearchandrescueindisasterareainthedomainofrobotisaburgeoningandcha11engingsubject[1].Minerescuerobotwasdevelopedtoentermines duringemergencies tolo catepossibleescaperoutesfor thosetrapped insideand determinewhetheritissafe forhuman toente ror not.Localizationisafundamentalprobleminthis field. Loca lizationmethodsbasedoncameracanbemainlyclassifiedintogeometric,topologicalorhybridones[2].Withitsfeasibilityandeffectiveness,scenerecognitionbecomesoneoftheimportanttechnologiesoftopologicallocalization.Current1ymostscenerecognitionmethodsarebasedongloba1imagefeaturesandhavetwodistinctstages:trainingoff1ineandmatchingon1ine.Duringthetrainingstage,robotcollectstheimagesoftheenvironmentwhereitworksandprocessestheimagestoextractg1oba1featuresthatrepresentthescene.Someapproacheswereusedtoanalyzethedata-setofimagedirect1yandsomeprimaryfeatureswerefound,suchasthePCAmethod[3].However,thePCAmethod is noteffectiveindistinguishing theclassesoffeatures. Another typeof approachusesappearancefeaturesincludingco1or,textureandedgedensitytorepresenttheimage.Forexample,ZHOUetal[4]usedmultidimensionalhistogramstodescribeg1obalappearance features.Thismethodiss i mplebut sensitive tosca1eand i1luminationchanges. Infact, al1kinds ofglobalimagefeaturesaresufferedfromthechangeofenvironment.LOWE[5]presentedaSIFTmethodthatusessimilarityinvariantdescriptorsformedbycharacteristicsealeandorientationatinterestpointstoobtainthefeatures.Thefeaturesareinvarianttoimagescaling,translation,rotationandpartia1lyinvarianttoilluminationchanges.ButSIFTmaygenerate1000ormoreinterestpoints,whichmayslowdowntheprocessordramatically.Duringthematching stage,nearestneig hbor strateg y(NN)iswide1yadoptedforits faci1ityandintel 1igi bil i ty[6]. Butitcannotcapturethecontributionofindividualfeatureforscenerecognition.In e xperiments,the NN is notgo odenoughtoexpressthesimi 1 aritybetweentwo pattern s .Fu rthermore, theselectedfeaturescannotrep resentthescenethoroughly accor dingtot hestate-of-artpatternrecognition,whichmakesrecognitionnotreliab1e[7].S ointhis workanewrecognitio nsystemispresented, whichis morereliab leandeffectiveifit isusedinacomplex mineenvironment.Inthissystem,weimprovetheinvariancebyextractingsalientlocalimageregionsas1andmarkstoreplacethewholeimagetodealwithlargechangesinscale,2Drotationandviewpoint.Andthenumberofinterestpointsisreducedeffectively,whichmakestheprocessingeasier.FuzzyrecognitionstrategyisdesignedtorecognizethelandmarksinplaceofNN,whichcanstrengthenthecontributionofindividualfeatureforscenerecognition.Becauseofits partialinformat i onresumingability,hiddenMarkovmodelisa doptedto organize those1andmarks,whichcancapture thestructureor relationshipamongthem.Soscenerecogni tioncanbe transformedtotheevaluationprob1emofHMM,whichmakesrecognitionrobust.Salient1ocalimageregionsdetectionResearchesonbio1ogicalvisionsystemindicatethatorganism(likedrosophi1a)oftenpaysattentiontocertainspecialregionsinthescenefortheirbehavioralre1evanceorlocalimagecueswhileobservingsurroundings[8].Theseregionscanbetakenasnaturallandmarkstoeffectivelyrepresentanddistinguishdifferentenvironments.Inspiredbythose,weusecenter-surrounddifferencemethodtodetectsalientregionsinmulti-scaleimagespaces.Theopponenciesofcolorandtexturearecomputedtocreatethesaliencymap.Follow-up,sub-imagecenteredatthesalientpositioninSistakenasthelandmarkregion.Thesizeofthe1andmarkregioncanbedecidedadaptivelyaccordingtothechangesofgradientorientationofthelocalimage[11].Mobilerobotnavigationrequiresthatnaturallandmarksshouldbedetectedstablywhenenvironmentschangetosomeextent.Tovalidatetherepeatabilityonlandmarkdetectionofourapproach,wehavedonesomeexperimentsonthecasesofscale,2Drotationandviewpointchangesetc.Fig.1showsthatthedoorisdetectedforitssaliencywhenviewpointchanges.Moredetailedanalysisandresultsaboutscaleandrotationcanbefoundinourpreviousworks[12].ScenerecognitionandlocalizationDifferentfromotherscenerecognitionsystems,oursystemdoesn’tneedtrainingoffline.Inotherwords,ourscenesarenotclassifiedinadvance.Whenrobotwanders,scenescapturedatintervalsoffixedtimeareusedtobuildthevertexofatopologicalmap,whichrepresentstheplacewhererobotlocates.Althoughthemap'sgeometriclayoutisignoredbythe1oca1izationsystem,itisusefulforvisualizationanddebugging[13]andbeneficialtopathplanning.Solocalizationmeanssearchingthebestmatchofcurrentsceneonthemap.InthispaperhiddenMarkovmodelisusedtoorganizetheextractedlandmarksfromcurrentsceneandcreatethevertexoftopologicalmapforitspartialinformationresumingabi1ity.Resembledbypanoramicvisionsystem,robotlooksaroundtogetomni-images.FromFig.1Experimentonviewpointchangeseachimage,salientlocalregionsaredetectedandformedtobeasequence,namedaslandmarksequencewhoseorderisthesameastheimagesequence.ThenahiddenMarkovmode1iscreatedbasedonthelandmarksequenceinvolvingksalientlocalimageregions,whichistakenasthedescriptionoftheplacewheretherobotlocates.InoursystemEVI-D70camerahasaviewfieldof±170°.Consideringtheoverlapeffect,wesampleenvironmentevery45°toget8images.Letthe8imagesashiddenstateSi(1<i<8),thecreatedHMMcanbeillustratedbyFig.2.TheparametersofHMM,aijandbjk,areachievedbylearning,usingBaulm-Welcha1gorithm[14].Thethresholdofconvergenceissetas0.001.Asfortheedgeoftopologicalmap,weassignitwithdistanceinformationbetweentwovertices.Thedistancescanbecomputedaccordingtoodometryreadings.Fig.2HMMofenvironmentTolocateitselfonthetopo1ogicalmap,robotmustrunits 'eye'onenvi ronment andextract a landmarksequence L1’-Lk‘,then search themapforthe bes tmatchedvertex(scene).Differentfromtraditionalprobab ilisticlocalization[15],in oursystemlo calization problemcan be convertedTOC\o"1-5"\h\ztotheeva1uationproblemofHMM.Theve rtex withthe greatesteva1uationva1ue,whichmus talso be gre aterthan athreshold,istakenasthebestmatched vertex, which indicatesthemostpossibleplacewheretherobotis .4Matchstrategybasedonfuzzy1ogicOneofthekeyissuesinimagematchproblemistochoosethemosteffectivefeaturesordescriptorstorepresenttheoriginalimage.Duetorobotmovement,thoseextractedlandmarkregionswillchangeatpixelleve1.So,thedescriptorsorfeatureschosenshouldbeinvarianttosomeextentaccordingtothechangesofscale,rotationandviewpointetc.Inthispaper,weuse4featurescommonlyadoptedinthecommunitythatarebrieflydescribedasfollows.GO:Gradientorientation.Ithasbeenprovedthatilluminationandrotationchangesarelikelytohavelessinfluenceonit[5].ASMandENT：Angularsecondmomentandentropy,whicharetwotexturedescriptors.H:Hue,whichisusedtodescribethefundamentalinformationoftheimage.Anotherkeyissueinmatchproblemistochooseagoodmatchstrategyoralgorithm.Usuallynearestneighborstrategy(NN)isusedtomeasurethesimilaritybetweentwopatterns.ButwehavefoundintheexperimentsthatNNcan,tadequatelyexhibittheindividualdescriptororfeature,scontributiontosimilaritymeasurement.AsindicatedinFig.4,theinputimageFig.4(a)comesfromdifferentviewofFig.4(b).ButthedistancebetweenFigs.4(a)and(b)computedbyJeffereydivergenceislargerthanFig.4(c).Tosolvetheproblem,wedesignanewmatchalgorithmbasedonfuzzylogicforexhibitingthesubtlechangesofeachfeatures.Thealgorithmisdescribedasbelow.Andthelandmarkinthedatabasewhosefusedsimilaritydegreeishigherthananyothersistakenasthebestmatch.ThematchresultsofFigs.2(b)and(c)aredemonstratedbyFig.3.Asindicated,thismethodcanmeasurethesimilarityeffectivelybetweentwopatterns.Fig.3Similaritycomputedusingfuzzystrategy5ExperimentsandanalysisThe1ocalizationsystemhasbeenimplementedonamobilerobot,whichisbui1tbyourlaboratory.ThevisionsystemiscomposedofaCCDcameraandaframe-grabberIVC-4200.Thereso1utionofimageissettobe400x320andthesamplefrequencyissettobe10frames/s.Thecomputersystemiscomposedof1GHzprocessorand512Mmemory,whichiscarriedbytherobot.Presentlytherobotworksinindoorenvironments.BecauseHMMisadoptedtorepresentandrecognizethescene,oursystemhastheabilitytocapturethediscriminationaboutdistributionofsalientlocalimageregionsanddistinguishsimilarsceneseffectively.Table1showstherecognitionresultofstaticenvironmentsincluding5 1anewaysandasilo.10scenesareselectedfromeachenvironmentandHMMsarecreatedforeachscene.Then20scenesarecol1ectedwhentherobotenterseachenvironmentsubsequent1ytomatchthe60HMMsabove.Inthetable,“truth“meansthatthescenetobelocalizedmateheswiththerightscene(theevaluationvalueofHMMis30%greaterthan thesecondhigh evaluati on),“Uncerta inty”mean sTOC\o"1-5"\h\zthatthe evaluationvalue ofHMM isgreater thanthe secondhi gh evaluationunder10%. “Errormatch” means tha tthescene to belocalized matcheswiththewrong scene.In thet able,theratiooferrormatchis0.Butitispossiblethatthescenetobelocalizedcan,tmatchanyscenesandnewvertexesarecreated.Furthermore,the“ratiooftruth”aboutsi1oislowerbecausesalientcuesarefewerinthiskindofenvironment.Intheperiodofautomaticexploring,simi1arscenescanbecombined.Theprocesscanbesummarizedas:whenloca1izationsucceeds,thecurrentlandmarksequenceisaddedtotheaccompanyingobservationsequenceofthematchedvertexun-repeatedlyaccordingtotheirorientation(includingtheangleoftheimagefromwhichthesalientlocalregionandtheheadingoftherobotcome).TheparametersofHMMarelearnedagain.Comparedwiththeapproachesusingappearancefeaturesofthewho1eimage(Method2,M2),oursystem(Ml)useslocalsalientregionstolocalizeandmap,whichmakesithavemoretoleranceofscale,viewpointchangescausedbyrobot,smovementandhigherratioofrecognitionandfeweramountofverticesonthetopologicalmap.So,oursystemhasbetterperformanceindynamicenvironment.ThesecanbeseeninTable2.Laneways1,2,4,5areinoperationwheresomeminersareworking,whichpuzzletherobot.6ConclusionsSalientlocalimagefeaturesareextractedtoreplacethewholeimagetoparticipateinrecognition,whichimprovethetoleranceofchangesinscale,2Drotationandviewpointofenvironmentimage.)Fuzzylogicisusedtorecognizethelocalimage,andemphasizetheindividualfeature’sontributiontorecognition,whichimprovesthereliabilityoflandmarks.HMMisusedtocapturethestructureorrelationshipofthoselocalimages,whichconvertsthescenerecognitionproblemintotheevaluationproblemofHMM.Theresultsfromtheaboveexperimentsdemonstratethattheminerescuerobotscenerecognitionsystemhashigherratioofrecognitionandlocalization.FutureworkwillbefocusedonusingHMMtodealwiththeuncertaintyoflocalization.中文翻译基于视觉的矿井救援机器人场景识别摘要：基于模糊逻辑和隐马尔可夫模型（HMM）,论文提出了一个新的场景识别系统，可应用于紧急情况下矿山救援机器人的定位。该系统使用单眼相机获取机器人所处位置的全方位的矿井环境图像。通过采用中心环绕差分法，从图像中提取突出的位置图像区域作为自然的位置标志。这些标志通过使用HMM有机组织起来代表机器人坐在场景,模糊逻辑算法用来匹配场景和位置标志。通过这种方式,定位问题，即系统的现场识别问题,可以转化为对HMM的评价问题。这些技术贡献使系统具有处理比率变化、二维旋转和视角变化的能力。实验结果还证明，该系统在静态和动态矿山环境中都具有较高的识别和定位的成功率。关键字：机器人定位；场景识别；突出图像匹配算法；模糊逻辑;隐马尔可夫模型1介绍在机器人领域搜索和救援灾区是一个新兴而富有挑战性的课题。矿井救援机器人的开发是为了在紧急情况下进入矿井为被困人员查找可能的逃生路线，并确定该线路是否安全。定位识别是这个领域的基本问题。基于摄像头的定位可以主要分为几何法、拓扑法或混合法。凭借其可行性和有效性,场景识别成为拓扑定位的重要技术之一。目前,大多数场景识别方法是基于全局图像特征，有两个不同的阶段:离线培训和在线匹配。在训练阶段，机器人收集其所工作环境的图像,并处理这些图像提取出能表征该场景的全局特征。一些方法直接分析图像数据得到一些基本特征，比如PCA方法。但是，PCA方法是不能区分特征的类别。另一种方法使用外观特征包括颜色、纹理和边缘密度来表示图像。例如，周等人用多维直方图来描述全局外观特征。此方法简单,但对比率和光照变化敏感。事实上，各种全局图像特征，所受来自环境变化的影响。LOWE提出了SIFT方法，该方法利用关注点尺度和方向所形成的描述的相似性获得特征。这些特征对于图像缩放、平移、旋转和局部光照不变是稳定的。但SIFT可能产生1000个或更多的兴趣点,这可能使处理器大大减慢。在匹配阶段，近邻算法(NN)因其简单和可行而被广泛采用。但是它并不能捕捉到个别特征对场景识别的贡献。在实验中,NN在表达两种部分之间的相似性时效果并不足够好。此外，所选的特征并不能彻底地按照国家模式识别标准表示场景，这使得识别结果不可靠。因此，在这些分析中提出了一种新的识别系统,如果使用在复杂的矿井环境中它将更加可靠和有效。在这个系统中，我们通过提取突出的图像局部区域作为位置标志用以替代整个图像,改善了信息的稳定性,从而处理比率、二维旋转和视角的变化。兴趣点数量有效减少,这使得处理更加容易。模糊识别算法用以识别邻近位置的位置标志，它可以增强个别特征对场景识别的作用。由于它的部分信息恢复能力，采用隐马尔可夫模型组织这些位置标志，它可以捕捉到的结构或标志之间的关系。因此，场景识别可以转化为对HMM评价问题，这使得识别具有鲁棒性。2局部图像区域不变形的检测生物视觉系统的研究表明,生物体(像果蝇)在观察周围环境时，经常因为他们的行为习惯注意场景中确定的特殊区域或者局部图像信息。这些区域可以当作天然的位置标志有效地表示和区别不同环境。受这些启示，我们利用中心环绕差分法检测多尺度图像空间突出的区域。计算颜色和纹理的相似度用以绘制突出区域的地图。随后,以地图突出位置为中心的分图像，被定义为位置标志区域。位置标志区域的大小可以根据该区域图像梯度方向的变化自适应决定。移动机器人的导航要求当环境有一定程度变化时自然位置标志能被稳定地检测出来。为了验证我们方法对位置标志检测的的可重复性，我们已经在图像比例、二维旋转和视角等变化时,做了一些实验。图1表明当视角变化时因为它的突出效果大门能被检测出来。关于比率和旋转更详细的分析和结果可以在我们以前的