matlab中英文翻譯文獻(xiàn)

附錄英文原文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.中文翻譯基于視覺的礦井救援機(jī)器人場景識別摘要：基于模糊邏輯和隱馬爾可夫模型（HMM）,論文提出了一個新的場景識別系統(tǒng)，可應(yīng)用于緊急情況下礦山救援機(jī)器人的定位。該系統(tǒng)使用單眼相機(jī)獲取機(jī)器人所處位置的全方位的礦井環(huán)境圖像。通過采用中心環(huán)繞差分法，從圖像中提取突出的位置圖像區(qū)域作為自然的位置標(biāo)志。這些標(biāo)志通過使用HMM有機(jī)組織起來代表機(jī)器人坐在場景,模糊邏輯算法用來匹配場景和位置標(biāo)志。通過這種方式,定位問題，即系統(tǒng)的現(xiàn)場識別問題,可以轉(zhuǎn)化為對HMM的評價問題。這些技術(shù)貢獻(xiàn)使系統(tǒng)具有處理比率變化、二維旋轉(zhuǎn)和視角變化的能力。實(shí)驗(yàn)結(jié)果還證明，該系統(tǒng)在靜態(tài)和動態(tài)礦山環(huán)境中都具有較高的識別和定位的成功率。關(guān)鍵字：機(jī)器人定位；場景識別；突出圖像匹配算法；模糊邏輯;隱馬爾可夫模型1介紹在機(jī)器人領(lǐng)域搜索和救援災(zāi)區(qū)是一個新興而富有挑戰(zhàn)性的課題。礦井救援機(jī)器人的開發(fā)是為了在緊急情況下進(jìn)入礦井為被困人員查找可能的逃生路線，并確定該線路是否安全。定位識別是這個領(lǐng)域的基本問題?；跀z像頭的定位可以主要分為幾何法、拓?fù)浞ɑ蚧旌戏ā{借其可行性和有效性,場景識別成為拓?fù)涠ㄎ坏闹匾夹g(shù)之一。目前,大多數(shù)場景識別方法是基于全局圖像特征，有兩個不同的階段:離線培訓(xùn)和在線匹配。在訓(xùn)練階段，機(jī)器人收集其所工作環(huán)境的圖像,并處理這些圖像提取出能表征該場景的全局特征。一些方法直接分析圖像數(shù)據(jù)得到一些基本特征，比如PCA方法。但是，PCA方法是不能區(qū)分特征的類別。另一種方法使用外觀特征包括顏色、紋理和邊緣密度來表示圖像。例如，周等人用多維直方圖來描述全局外觀特征。此方法簡單,但對比率和光照變化敏感。事實(shí)上，各種全局圖像特征，所受來自環(huán)境變化的影響。LOWE提出了SIFT方法，該方法利用關(guān)注點(diǎn)尺度和方向所形成的描述的相似性獲得特征。這些特征對于圖像縮放、平移、旋轉(zhuǎn)和局部光照不變是穩(wěn)定的。但SIFT可能產(chǎn)生1000個或更多的興趣點(diǎn),這可能使處理器大大減慢。在匹配階段，近鄰算法(NN)因其簡單和可行而被廣泛采用。但是它并不能捕捉到個別特征對場景識別的貢獻(xiàn)。在實(shí)驗(yàn)中,NN在表達(dá)兩種部分之間的相似性時效果并不足夠好。此外，所選的特征并不能徹底地按照國家模式識別標(biāo)準(zhǔn)表示場景，這使得識別結(jié)果不可靠。因此，在這些分析中提出了一種新的識別系統(tǒng),如果使用在復(fù)雜的礦井環(huán)境中它將更加可靠和有效。在這個系統(tǒng)中，我們通過提取突出的圖像局部區(qū)域作為位置標(biāo)志用以替代整個圖像,改善了信息的穩(wěn)定性,從而處理比率、二維旋轉(zhuǎn)和視角的變化。興趣點(diǎn)數(shù)量有效減少,這使得處理更加容易。模糊識別算法用以識別鄰近位置的位置標(biāo)志，它可以增強(qiáng)個別特征對場景識別的作用。由于它的部分信息恢復(fù)能力，采用隱馬爾可夫模型組織這些位置標(biāo)志，它可以捕捉到的結(jié)構(gòu)或標(biāo)志之間的關(guān)系。因此，場景識別可以轉(zhuǎn)化為對HMM評價問題，這使得識別具有魯棒性。2局部圖像區(qū)域不變形的檢測生物視覺系統(tǒng)的研究表明,生物體(像果蠅)在觀察周圍環(huán)境時，經(jīng)常因?yàn)樗麄兊男袨榱?xí)慣注意場景中確定的特殊區(qū)域或者局部圖像信息。這些區(qū)域可以當(dāng)作天然的位置標(biāo)志有效地表示和區(qū)別不同環(huán)境。受這些啟示，我們利用中心環(huán)繞差分法檢測多尺度圖像空間突出的區(qū)域。計(jì)算顏色和紋理的相似度用以繪制突出區(qū)域的地圖。隨后,以地圖突出位置為中心的分圖像，被定義為位置標(biāo)志區(qū)域。位置標(biāo)志區(qū)域的大小可以根據(jù)該區(qū)域圖像梯度方向的變化自適應(yīng)決定。移動機(jī)器人的導(dǎo)航要求當(dāng)環(huán)境有一定程度變化時自然位置標(biāo)志能被穩(wěn)定地檢測出來。為了驗(yàn)證我們方法對位置標(biāo)志檢測的的可重復(fù)性，我們已經(jīng)在圖像比例、二維旋轉(zhuǎn)和視角等變化時,做了一些實(shí)驗(yàn)。圖1表明當(dāng)視角變化時因?yàn)樗耐怀鲂Ч箝T能被檢測出來。關(guān)于比率和旋轉(zhuǎn)更詳細(xì)的分析和結(jié)果可以在我們以前的