matlab图像处理外文翻译外文文献英文文献基于视觉的矿井救援.docx

matlab图像处理外文翻译外文文献英文文献基于视觉的矿井救援.docx

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matlab图像处理外文翻译外文文献英文文献基于视觉的矿井救援

附录A英文原文

Scenerecognitionforminerescuerobot

localizationbasedonvision

CUIYi—an（崔益安）,CAIZi-xing（蔡自兴），WANGLu（王璐）

Abstract：

AnewscenerecognitionsystemwaspresentedbasedonfuzzylogicandhiddenMarkovmodel（HMM）thatcanbeappliedinminerescuerobotlocalizationduringemergencies。

Thesystemusesmonocularcameratoacquireomni-directionalimagesofthemineenvironmentwheretherobotlocates.Byadoptingcenter-surrounddifferencemethod,thesalientlocalimageregionsareextractedfromtheimagesasnaturallandmarks。

TheselandmarksareorganizedbyusingHMMtorepresentthescenewheretherobotis，andfuzzylogicstrategyisusedtomatchthesceneandlandmark.Bythisway，thelocalizationproblem,whichisthescenerecognitionprobleminthesystem，canbeconvertedintotheevaluationproblemofHMM.Thecontributionsoftheseskillsmakethesystemhavetheabilitytodealwithchangesinscale，2Drotationandviewpoint.Theresultsofexperimentsalsoprovethatthesystemhashigherratioofrecognitionandlocalizationinbothstaticanddynamicmineenvironments.

Keywords：

robotlocation；scenerecognition；salientimage；matchingstrategy；fuzzylogic;hiddenMarkovmodel

1Introduction

Searchandrescueindisasterareainthedomainofrobotisaburgeoningandchallengingsubject[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。

2Salientlocalimageregionsdetection

Researchesonbiologicalvisionsystemindicatethatorganism（likedrosophila）oftenpaysattentiontocertainspecialregionsinthescenefortheirbehavioralrelevanceorlocalimagecueswhileobservingsurroundings[8］.Theseregionscanbetakenasnaturallandmarkstoeffectivelyrepresentanddistinguishdifferentenvironments.Inspiredbythose,weusecenter—surrounddifferencemethodtodetectsalientregionsinmulti—scaleimagespaces.Theopponenciesofcolorandtexturearecomputedtocreatethesaliencymap。

Follow-up，sub—imagecenteredatthesalientpositioninSistakenasthelandmarkregion。

Thesizeofthelandmarkregioncanbedecidedadaptivelyaccordingtothechangesofgradientorientationofthelocalimage[11］。

Mobilerobotnavigationrequiresthatnaturallandmarksshouldbedetectedstablywhenenvironmentschangetosomeextent.Tovalidatetherepeatabilityonlandmarkdetectionofourapproach，wehavedonesomeexperimentsonthecasesofscale,2Drotationandviewpointchangesetc.Fig。

1showsthatthedoorisdetectedforitssaliencywhenviewpointchanges。

Moredetailedanalysisandresultsaboutscaleandrotationcanbefoundinourpreviousworks［12］。

3Scenerecognitionandlocalization

Differentfromotherscenerecognitionsystems,oursystemdoesn'tneedtrainingoffline。

Inotherwords，ourscenesarenotclassifiedinadvance.Whenrobotwanders,scenescapturedatintervalsoffixedtimeareusedtobuildthevertexofatopologicalmap,whichrepresentstheplacewhererobotlocates。

Althoughthemap'sgeometriclayoutisignoredbythelocalizationsystem,itisusefulforvisualizationanddebugging[13］andbeneficialtopathplanning。

Solocalizationmeanssearchingthebestmatchofcurrentsceneonthemap.InthispaperhiddenMarkovmodelisusedtoorganizetheextractedlandmarksfromcurrentsceneandcreatethevertexoftopologicalmapforitspartialinformationresumingability.

Resembledbypanoramicvisionsystem，robotlooksaroundtogetomni-images。

From

Fig。

1Experimentonviewpointchanges

eachimage,salientlocalregionsaredetectedandformedtobeasequence，namedaslandmarksequencewhoseorderisthesameastheimagesequence。

ThenahiddenMarkovmodeliscreatedbasedonthelandmarksequenceinvolvingksalientlocalimageregions,whichistakenasthedescriptionoftheplacewheretherobotlocates.InoursystemEVI—D70camerahasaviewfieldof±170°.Consideringtheoverlapeffect,wesampleenvironmentevery45°toget8images.

Letthe8imagesashiddenstateSi（1≤i≤8），thecreatedHMMcanbeillustratedbyFig.2.TheparametersofHMM，aijandbjk,areachievedbylearning，usingBaulm-Welchalgorithm[14］。

Thethresholdofconvergenceissetas0.001。

Asfortheedgeoftopologicalmap,weassignitwithdistanceinformationbetweentwovertices。

Thedistancescanbecomputedaccordingtoodometryreadings。

Fig。

2HMMofenvironment

Tolocateitselfonthetopologicalmap，robotmustrunits‘eye'onenvironmentandextractalandmarksequenceL1′−Lk′,thensearchthemapforthebestmatchedvertex（scene）.Differentfromtraditionalprobabilisticlocalization[15］,inoursystemlocalizationproblemcanbeconvertedtotheevaluationproblemofHMM.Thevertexwiththegreatestevaluationvalue，whichmustalsobegreaterthanathreshold，istakenasthebestmatchedvertex,whichindicatesthemostpossibleplacewheretherobotis.

4Matchstrategybasedonfuzzylogic

Oneofthekeyissuesinimagematchproblemistochoosethemosteffectivefeaturesordescriptorstorepresenttheoriginalimage。

Duetorobotmovement,thoseextractedlandmarkregionswillchangeatpixellevel。

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.3Similaritycomputedusingfuzzystrategy

5Experimentsandanalysis

Thelocalizationsystemhasbeenimplementedonamobilerobot，whichisbuiltbyourlaboratory。

ThevisionsystemiscomposedofaCCDcamera