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，可應用于緊急情況下礦山救援機器人的定位。該系統使用單眼相機獲取機器人所處位置的全方位的礦井環境圖像。通過采用中心環繞差分法，從圖像中提取突出的位置圖像區域作為自然的位置標志。這些標志通過使用HMM有機組織起來代表機器人坐在場景,模糊邏輯算法用來匹配場景和位置標志。通過這種方式,定位問題，即系統的現場識別問題,可以轉化為對HMM的評價問題。這些技術貢獻使系統具有處理比率變化、二維旋轉和視角變化的能力。實驗結果還證明，該系統在靜態和動態礦山環境中都具有較高的識別和定位的成功率。關鍵字：機器人定位；場景識別；突出圖像匹配算法；模糊邏輯;隱馬爾可夫模型1介紹在機器人領域搜索和救援災區是一個新興而富有挑戰性的課題。礦井救援機器人的開發是為了在緊急情況下進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。定位識別是這個領域的基本問題?；跀z像頭的定位可以主要分為幾何法、拓撲法或混合法。憑借其可行性和有效性,場景識別成為拓撲定位的重要技術之一。目前,大多數場景識別方法是基于全局圖像特征，有兩個不同的階段:離線培訓和在線匹配。在訓練階段，機器人收集其所工作環境的圖像,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據得到一些基本特征，比如PCA方法。但是，PCA方法是不能區分特征的類別。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。事實上，各種全局圖像特征，所受來自環境變化的影響。LOWE提出了SIFT方法，該方法利用關注點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、旋轉和局部光照不變是穩定的。但SIFT可能產生1000個或更多的興趣點,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。在實驗中,NN在表達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準表示場景，這使得識別結果不可靠。因此，在這些分析中提出了一種新的識別系統,如果使用在復雜的礦井環境中它將更加可靠和有效。在這個系統中，我們通過提取突出的圖像局部區域作為位置標志用以替代整個圖像,改善了信息的穩定性,從而處理比率、二維旋轉和視角的變化。興趣點數量有效減少,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志，它可以增強個別特征對場景識別的作用。由于它的部分信息恢復能力，采用隱馬爾可夫模型組織這些位置標志，它可以捕捉到的結構或標志之間的關系。因此，場景識別可以轉化為對HMM評價問題，這使得識別具有魯棒性。2局部圖像區域不變形的檢測生物視覺系統的研究表明,生物體(像果蠅)在觀察周圍環境時，經常因為他們的行為習慣注意場景中確定的特殊區域或者局部圖像信息。這些區域可以當作天然的位置標志有效地表示和區別不同環境。受這些啟示，我們利用中心環繞差分法檢測多尺度圖像空間突出的區域。計算顏色和紋理的相似度用以繪制突出區域的地圖。隨后,以地圖突出位置為中心的分圖像，被定義為位置標志區域。位置標志區域的大小可以根據該區域圖像梯度方向的變化自適應決定。移動機器人的導航要求當環境有一定程度變化時自然位置標志能被穩定地檢測出來。為了驗證我們方法對位置標志檢測的的可重復性，我們已經在圖像比例、二維旋轉和視角等變化時,做了一些實驗。圖1表明當視角變化時因為它的突出效果大門能被檢測出來。關于比率和旋轉更詳細的分析和結果可以在我們以前的