matlab中英文翻译文献

附录英文原文

Sceｎe rｅcognitｉon for minｅrescue robot

ｌocalizａtｉon bａsed ｏn vision

Abstract：Ａneｗsｃeneｒecognitiｏn system waｓpｒeseｎｔed bａｓe ｄoｎｆuzzy ｌoｇiｃand hiddｅn Markov mｏｄeｌ（HMM) tｈat caｎb ｅaｐpｌiｅd in minｅresｃｕe robot loｃalizａtion ｄuｒing ｅmergｅｎc ｉｅs. Thｅsyｓtem usｅs monocuｌar ｃamera ｔo aｃquire oｍni－ｄireｃtional images oｆｔhe mine eｎvironment wｈeｒe the rｏbｏt locａteｓ. Ｂy ａdoｐｔｉｎｇｃｅｎter-surround ｄifferencｅmethｏd, tｈe ｓａliｅnt locaｌimage regioｎs ａre extractｅd from the imａges as nａtural landｍａｒｋs．Ｔｈeｓe landｍarｋs are oｒｇａｎｉzed by uｓｉng HＭM to repｒｅsｅnｔｔhe scene wheｒe ｔhe roｂｏt is,and fuzzｙlogiｃｓtrateｇｙｉs used to mａtch the sｃene aｎdｌandmark．By thｉs way, the lｏcaｌizaｔiｏｎproｂlem，which ｉs the sceneｒeｃｏｇnitiｏn probｌem in t ｈｅsysｔem，can be conｖerted into the evａｌuationｐｒｏbｌｅｍｏf HMM. Ｔｈe contriｂｕtｉonｓoｆtｈeｓe skiｌls make the system ｈａｖｅｔhｅａbｉlityｔｏdｅal with changes iｎscale, 2D rotａｔion and viｅwpoｉnt. Tｈe reｓulｔs oｆｅxperiｍｅｎｔsａｌsｏproｖe thａt the system haｓhigher rａtio ｏf reｃｏgnition aｎd locａｌizatｉoｎin botｈｓtatiｃanｄdynamiｃmｉne environments．

Keｙwords: roboｔlocatｉｏn;scene recognｉtiｏn；sａlｉenｔｉmａｇe; mａtching sｔrａtegｙ；fuzzy ｌogic; hidden Ｍarkov mｏdｅｌ

1 Intｒｏdｕctioｎ

Sｅarch and ｒesｃｕｅｉn disasteｒarea iｎｔhe domain oｆｒobot i ｓ a burｇeoning and chaｌｌengｉng suｂjecｔ[1]. Minｅrｅscue robｏｔwas devｅloped tｏｅntｅr miｎeｓdｕring emerｇｅnciｅs to loｃaｔe possｉblｅeｓｃａpe ｒoｕtｅs for ｔhose tｒapped ｉnｓiｄe and ｄeｔermine whｅｔheｒｉｔiｓsaｆｅfｏr humａｎｔo enteｒorｎoｔ. Loｃalizaｔion is a fundamentａl ｐｒｏblｅm in thｉs ｆielｄ．Lｏcａlizaｔion metｈoｄｓbaｓed ｏn caｍera caｎbe mａinly classiｆied intｏgｅoｍetr ｉc, topoloｇiｃal ｏr ｈybrid onｅs[2］．With ｉｔs feasibility ａnｄeffectiv ｅneｓs, sceｎe rｅcogniｔｉon becｏmes oｎｅof ｔhe imｐortａnt tｅcｈnologies of topｏlｏgiｃａl localｉzａtiｏn．

Cｕrrentｌｙmｏst scene rｅｃognｉtiｏn methods are baseｄoｎgloba ｌimａge fｅatｕｒes aｎｄｈａve tｗｏｄistiｎct stages:ｔraininｇｏｆf ｌine and ｍaｔｃhinｇoｎｌｉnｅ.

During tｈe training sｔage,roboｔcollｅctｓtｈｅiｍages of ｔhｅeｎviｒonmenｔwhere itｗorｋs aｎd prｏcｅssｅs the iｍagｅs to eｘtrac ｔgｌoｂaｌfeatuｒeｓｔhat ｒepｒｅｓｅnt the ｓｃene. Somｅappｒoacｈeｓwｅre used to ａnalｙｚe thｅdａtａ-set of ｉmage dｉrectｌy ａｎd ｓｏｍe ｐriｍａry feａｔures were found, suｃｈas tｈe PCA ｍｅｔhod [3］. Howevｅr，tｈｅPCA ｍethoｄiｓnｏt efｆeｃtive in dｉｓtin ｇｕiｓhiｎg tｈe clａsses of ｆｅａtures．Anotherｔyｐｅofａｐproach ｕseｓａｐpeａrａnｃｅfｅatures including coｌor, texｔｕre anｄｅdｇe deｎsity toｒepresent ｔｈe imａｇｅ．For eｘample, ＺＨOＵe ｔal[４］usｅd ｍultiｄimensionａl histｏgrams ｔo descｒibｅgｌｏｂａl ａpｐeaｒance features. This method iｓsｉmple bｕｔsｅnsｉtiｖｅｔo ｓcａｌｅand iｌluｍinａtｉon chanｇｅs.Ｉnｆａcｔ, alｌkiｎdｓoｆglobal iｍage features aｒe sufｆereｄfrｏm the cｈange of envirｏnｍent.

LOWE [5] ｐresenteｄa SIFＴmethoｄthａt uses ｓimilａrity invarｉant desｃripｔors fｏrmed ｂy ｃharacteristic sｃａlｅａｎｄorｉｅntatｉoｎat intereｓt pointｓto obｔain tｈｅfeaｔureｓ.The ｆｅaturesａre iｎｖariａntｔｏimagｅscaling, ｔraｎslaｔｉｏn,rotａｔｉoｎanｄpaｒｔiaｌly invａriant tｏilluｍinａtion cｈanges．But SＩFTｍay generate 1000 or mｏre intereｓt ｐoinｔs, ｗhich may sloｗdown tｈe pｒocesｓoｒdramaｔically.

Duｒing thｅmatching stage, ｎeareｓt neiｇhboｒstrａtｅｇy(ＮN）is wideｌy adopted for its faciｌｉty and ｉｎtｅlｌigｉbilｉty[6]. But it caｎｎoｔcaptuｒe the cｏnｔriｂｕtiｏn ｏfｉndiviｄｕaｌfｅature for ｓcenｅreｃognitioｎ．Inｅxｐeｒiｍｅntｓ, theＮN ｉs not goｏd enough to ｅxpreｓｓtｈe sｉmiｌarity ｂeｔween twｏpaｔternｓ. Ｆuｒｔhｅrmo ｒe，ｔhe seleｃteｄfeaｔｕresｃan not reｐreseｎt the scene thoroughlｙａc ｃｏｒding ｔｏtｈe sｔａte-ｏf-ａｒt ｐaｔterｎrecｏgｎition, ｗhicｈmakes recognitioｎnot reliａbｌｅ［7].

Ｓo ｉnｔhis worｋ a new recogｎｉtiｏn sｙstem is pｒesｅnｔed，wh ｉch is ｍore reliaｂle and ｅffectｉve if ｉtｉsｕsed ｉn a complexｍin ｅenviroｎmenｔ. In ｔｈis syｓtｅm，wｅimprove ｔhe invariａｎce by extrａctinｇsalieｎt lｏｃal image rｅgions as ｌａndｍａrkｓｔo replace ｔｈｅwhole iｍage to dｅal ｗitｈlarge changｅs iｎscａle,2D rotatio ｎand viewpoint. Ａｎd ｔhe nｕｍber ｏf ｉnteresｔｐｏints is rｅduceｄｅffecｔiｖely,ｗhiｃh makes ｔhe processing easier．Fuzzy rｅcognｉtion ｓtrａｔegy is designed to ｒｅｃｏgnizｅｔhe laｎdmａｒks iｎplaｃeｏf NＮ, wｈich can streｎｇthｅn the contriｂuｔion of indiｖｉdual fｅature for sｃene ｒecogｎitiｏn．Ｂecauｓe of iｔｓparｔial iｎｆormatｉon ｒｅsuｍing ability, ｈidden Markov mｏｄel is aｄopted to ｏrｇanizｅthｏｓe ｌａｎｄmａｒｋｓ, whｉｃh cａn ｃapｔure tｈe ｓtructure orｒelatiｏnshｉp aｍong them．Sｏsｃeｎe ｒｅcｏgniｔiｏｎｃaｎbｅｔransformed ｔo tｈｅevaluａｔiｏn probｌem of HＭＭ,whｉcｈmａkes recｏgnition robust.

2 Salienｔｌocａl iｍagｅregions detecｔｉon

Rｅseａrcheｓｏｎbｉoｌogｉcal vｉsｉon system inｄiｃａｔe that ｏrganism(likｅdroｓophiｌa) oftｅn ｐaｙs attenｔｉoｎto certaｉn ｓｐeci ａｌregions in the scenｅfoｒtｈｅｉｒbeｈａvioral reｌevance or local i ｍage ｃues whｉｌｅｏbserｖｉnｇsurrｏundings [8]. These ｒegions ｃan be taｋeｎas naｔｕｒal lａndmaｒks to effeｃtiｖelｙrepresenｔａnd ｄisｔｉngｕｉsh difｆerenｔenｖironmeｎtｓ．Inspireｄby tｈose，we ｕse cｅnteｒ-ｓurrounｄdiffｅrence ｍethod to deｔeｃｔsalient regｉons in mｕｌtｉ－scale ｉmａge spaces．Ｔhe opponencieｓof color and textｕre a ｒe compuｔeｄtｏｃreａteｔhe saliency ｍap.

Follｏw-up,sｕｂ-ｉmａｇeｃｅnteredａt the ｓalient position in S ｉs ｔaken aｓthe lａnｄmark region. The ｓizｅof ｔhe ｌandmark rｅgio ｎｃan ｂe ｄecided adaptｉvｅlｙaｃｃoｒding ｔo ｔhｅcｈaｎges of ｇraｄient orientatiｏn ofｔhe local iｍage[11].

Moｂiｌe robotｎaviｇation rｅquirｅs thaｔnａtｕrａl landmarｋs ｓｈould be deteｃｔed sｔａbly wheｎenvironments cｈaｎge ｔo somｅextｅnt. To ｖalｉdａte theｒepeaｔａbilｉty on laｎｄmark ｄetection ｏｆｏuｒaｐprｏａcｈ，we have done some expeｒiments ｏｎthe cａses oｆscale, ２D rotation and viｅwpoinｔｃhaｎges eｔc. Fig.1 ｓhoｗs tｈaｔtｈｅdｏoｒis ｄeｔectｅd for iｔs saliency wｈen viewｐｏint changeｓ. More dｅtaｉlｅｄａnalyｓiｓand rｅsults ａbouｔscａle and rotation caｎｂe ｆｏｕnd iｎour prｅvｉoｕｓworks［１2].

3 Scenｅｒeｃogｎｉtionａnd lｏcalizaｔion

Differenｔfｒｏm other scｅnｅreｃoｇｎｉtｉoｎｓysｔems, our sｙstem doｅｓn’t need ｔｒainｉng ｏｆｆlｉｎe.In otｈerｗordｓ,ｏur sｃenｅs are not classifieｄiｎaｄvanｃe.Wheｎrｏbotｗａnders, scｅnes ca ｐtｕｒeｄaｔinｔerｖals of fixed tｉmｅaｒe usｅd to build the vｅｒｔeｘof ａｔopological maｐ,which repｒｅｓｅnts the plａce whｅre robｏt lｏcａtes. Althｏugh ｔhe mａp’ｓgeｏmetrｉｃlayｏut iｓｉgnorｅｄｂｙ

ｔhe ｌocaｌiｚaｔion ｓysteｍ, iｔis useful fｏr visualｉzaｔion anｄdeb ｕgging[13]and benefiｃｉａｌtｏpaｔh planｎｉng．Ｓo localｉｚation m ｅａnｓsearchingｔhe bｅst match of curｒeｎｔscene oｎｔhｅｍａp.Ｉn ｔhiｓpaper hidｄeｎＭarkov model ｉs useｄto organize the eｘtｒａcｔed landmａrks from currｅnt scｅne anｄcreaｔe thｅｖeｒtｅx o ｆtｏpｏｌｏgicalｍap foｒｉts paｒtｉａl iｎformaｔioｎresuming ａbi ｌiｔy.

Rｅsｅmｂled ｂy pａnoramic vision ｓysｔem, rｏbｏt loｏks arｏｕnｄto get omｎi-ｉmaｇｅｓ. Frｏm

Fiｇ.1 Eｘpｅriｍenｔon vieｗpｏint changeｓ

eａch imａgｅ, salient locａl rｅgioｎs are detecｔeｄaｎd ｆoｒmed to be a sequencｅ, ｎａmed aｓlａndmark sequeｎcｅｗhose order is tｈe sａmｅas thｅiｍage sequencｅ. Then a hｉdden Maｒｋov modeｌｉs ｃreat ｅd baseｄon the landmａｒk ｓeｑueｎｃe ｉnvolviｎｇk ｓａlient local ｉｍage regiｏｎs，ｗhｉｃｈｉs taken as the descｒiptｉon of tｈｅplaｃｅwhｅｒe tｈe rｏbot loｃateｓ. In oｕｒsｙsteｍEVI-D70 caｍeraｈas a view fiｅｌd ｏf ±１70°．Coｎsidｅｒｉng the oｖeｒｌap effect,we sample

enｖｉrｏnｍent every 45° to gｅt８iｍages.

Let ｔｈe ８ｉmａgｅs ａs ｈiddｅｎsｔａtｅSi(1≤i≤８）,the cｒｅated HMＭcan be illustrａted by Fｉｇ.2．The paｒameｔers ｏf HＭM，aij anｄｂｊk, arｅａchｉeｖed by learning, using Baulm-Wｅlch aｌgｏｒiｔh ｍ［14］.The threshold of ｃｏnｖergence is sｅt as 0.0０1.

Aｓfor tｈe eｄge of ｔopolｏgiｃａl map,we assign it witｈdｉｓｔａｎcｅinfｏrｍaｔｉoｎbetwｅen two vertices. The disｔances can be ｃo ｍputed accorｄiｎg tｏｏdｏmetry readiｎgｓ.

Fig.2 HＭM ｏf enｖｉronmｅnｔ

To locate iｔself on ｔｈｅtｏpoｌogicａl map,roｂoｔｍuｓt ru ｎｉtｓ‘eye’ onｅnviｒoｎment aｎｄextrａｃｔ a lanｄｍaｒk sequence Ｌ1′ ?Ｌk′ , theｎｓearｃh the map for the beｓtｍatｃｈed verteｘ（sｃｅne). Difｆerｅnt ｆｒom traditional ｐrobａｂilｉstic lｏcalizatｉon[15], in ｏuｒsyｓｔem loｃａlizationｐrｏblem ｃａｎbｅｃoｎvertｅd tｏthe eｖaｌuatｉoｎprｏｂlｅmｏf HＭM. The vｅrtｅx wｉｔｈthe g ｒeateｓt ｅｖaｌuation vaｌue, which musｔalsｏbe ｇｒｅatｅｒthan a thrｅshｏld，is taken as the best matchｅｄvertex，whichｉｎdicaｔes the most ｐossible placｅｗhere thｅｒｏbot iｓ.

4 Ｍatch ｓtｒatｅgy ｂaｓed on fuzzyｌogic

One oｆthe kｅy issｕes in iｍａge ｍatcｈpｒｏｂｌeｍｉｓtｏｃhoosｅthe most ｅffectivｅfeaｔures oｒdeｓcｒｉptors ｔo repreｓｅｎt thｅoriginal ｉmage. Dｕe tｏrobot movement,ｔhose extｒactｅd landmaｒｋregｉｏns ｗｉll changｅaｔｐixel lｅveｌ. Sｏ, tｈe deｓｃｒiptoｒs oｒfeaｔuｒeｓchｏsen shｏuld be ｉnvａrｉanｔto soｍe exteｎt accｏrdiｎg tｏｔhe changes ｏf ｓcaｌｅ，rotaｔioｎaｎd vｉewpointｅtc. Ｉn ｔhis

ｐaper, wｅuｓe 4 feaｔures comｍonly adoptｅd in thｅｃommunｉty tha ｔａｒe brieflｙdesｃriｂed as ｆoｌlows.

ＧO:Gｒadient orｉentation.It hａｓbeeｎproved ｔhａｔiｌluminati ｏｎand ｒotaｔion changｅs are likelｙto hａve ｌess influence oｎit ［5].

ASMａnd ENT：Angular sｅcoｎｄmomｅnｔａnd entropy，whｉchａre twｏtexture descriｐtors.

Ｈ: Hue,which ｉs used to ｄesｃrｉbe the fundaｍｅｎtａl ｉnformation of the iｍaｇe．

Anotheｒｋey issue in match pｒｏblem iｓto choｏse a good match str ａteｇy or aｌgorithｍ.Usually neaｒest ｎeighｂor ｓtrａｔegy （NＮ) isｕs ｅd to measurｅtｈe similariｔy betｗｅen twｏpatterｎs. But we ｈａvｅfound ｉn thｅｅｘperimenｔs that NN can’t ａdｅｑuaｔｅｌy exhibit thｅindｉvidual descriptor orｆeatｕre’s cｏntｒibuｔioｎto similａrｉty measuremeｎt．Aｓinｄiｃａｔeｄin Fig．4, ｔhｅinput imａｇe Fig．4(a) cｏmes froｍｄｉfferent vieｗof Fig.４（b）. But tｈe dｉstaｎcｅbetw ｅen Ｆiｇs.4（a）aｎｄ(b）compuｔed by Ｊeffereｙｄｉvｅrgenｃe ｉs larger ｔhan Ｆig.4(c)．

To soｌve ｔhe problem,we design a new matcｈalｇoｒiｔhｍbased on fuzzy loｇｉc ｆoｒｅxhibiting ｔｈe subtｌｅchａngeｓof eaｃｈｆe ａtures. The alｇorithｍis described asｂｅlow.

And the landmａrk in thｅdaｔaｂase ｗhｏｓeｆｕsｅｄｓｉｍilarit ｙdegree iｓｈighｅrｔhaｎany othｅｒs is taｋen ａs tｈe best maｔc ｈ．Ｔhe mａｔchｒesuｌtｓof Ｆｉgｓ.２(b)and(ｃ) are dｅmonsｔｒa ｔed by Fig．３.As ｉndicatｅｄ，this ｍeｔhｏd caｎmeａｓure tｈe similar ｉｔy eｆfectiｖｅｌｙbeｔwｅeｎtwo patterｎs.

Fig.3Simｉlarity compｕtｅｄusiｎｇfuｚｚy stｒａtegy

５Ｅxｐeriments and ａｎalｙsｉs

Thｅｌocaliｚａtion syｓｔｅm has been iｍｐleｍeｎted oｎａmｏbile robot, whｉch is buiｌｔby ｏuｒlabｏrａtｏｒy.Ｔhｅvｉsion syｓｔem is composeｄof ａＣCD ｃａmerａaｎdａfraｍe-grabｂeｒIVC-420０. Ｔhe resoｌutiｏnｏｆimage iｓsｅt to bｅ4０0×32０and thｅｓample fｒeqｕｅnｃy iｓseｔto ｂe 10 frames/ｓ. Thｅｃoｍputeｒs ｙｓtem is composｅd oｆ１GHz processor and51２M ｍemory，which i ｓcarriedｂyｔhｅｒｏbｏt. Preｓenｔly the robot works in ｉｎｄoor ｅｎviｒonmｅｎts.

Becauｓe HＭM is adopｔed ｔo rｅｐresent aｎd recogｎize ｔhe ｓcene, oｕr ｓyｓtｅm has the aｂiliｔｙto capture tｈe ｄｉsｃriｍiｎatioｎaboｕt ｄistributiｏn of ｓaliｅnt local ｉmagｅregiｏns and dｉstinguish siｍｉlarｓｃenes effecｔively. Tａble 1shows tｈｅｒecｏgnｉｔｉon ｒｅsult of statｉc enviｒｏnmｅnｔs ｉnclｕdｉng ５ｌaneｗａys aｎd a silo. 10 ｓcｅnｅs are selectｅd ｆｒoｍeaｃh enviｒonｍent and HMMs arｅｃｒe ａted for each sｃene．Theｎ２0 sｃｅｎes arｅｃolｌｅctedｗｈen the robｏt enters eacｈenｖironｍentｓuｂseqｕｅnｔｌy toｍatch the 60

HMMs aｂｏｖe.

Ｉn tｈe table, “truth” ｍeans that the scene to be localiｚed matcｈｅs ｗitｈtｈe right scｅne （ｔhe evaluａtioｎvaluｅｏfＨMM is ３0% greａter tｈaｎthe second hｉgh ｅvalｕatｉoｎ). “Ｕncｅrtaｉnｔｙ” mｅanｓthat ｔhｅevaluatiｏn valｕｅｏf HMＭiｓｇreaｔeｒｔhaｎtｈeｓeconｄｈｉgh eｖａluatiｏn ｕnder 10%.“Error match” mｅaｎｓｔhaｔthe sｃene to ｂｅloｃａlized matches witｈthe ｗｒong scene. In the tａｂle, the ｒａtio of error mａtch is 0. But it is possiｂle ｔhat tｈe sｃenｅto be loｃａliｚed can’t match any sｃeneｓａndｎｅｗverｔeｘes ａre created. Fuｒthｅrmore, ｔｈｅ“ratｉo of trｕtｈ” abouｔsiｌo is lｏｗer bec ａｕseｓaliｅｎt cues ａrｅfewer in tｈis kiｎd of eｎviｒonment.

Iｎtｈe perｉoｄof aｕtomatｉｃexploriｎg, sｉmiｌar sｃenes can be coｍbineｄ. Ｔｈe prｏcesｓcａｎｂe summaｒizｅd aｓ: whｅn loｃaｌization succeｅdｓ, ｔhｅｃurｒent laｎdmarｋsｅquｅncｅis addｅd to theａccｏmpａnying obseｒvation sｅqueｎcｅｏf the matched vｅrteｘun-repeatedly according to theiｒorｉentaｔion （iｎclｕdｉng ｔhe angle oｆtheｉmaｇｅfrom wｈｉcｈthe salieｎｔlocal regｉon and ｔhe hｅadi ｎg of the roboｔcoｍｅ)．The paramｅters ofＨMＭarｅlｅarnｅd aｇain.

Cｏmpaｒｅd wｉth tｈｅapproaches using appeａrａncｅｆeatuｒｅs of the whoｌｅimａge(Methｏd2, Ｍ2),ouｒsｙstem (Ｍ1）uses loｃａl salｉｅnt regｉonｓto localizｅanｄmap, whiｃｈmａkes it ｈａve moｒe ｔolerance of scale，viｅwpoiｎt chａnges cａuｓed by ｒｏbot’ｓm ｏvemeｎt aｎｄhｉｇher ratiｏoｆｒecｏgnｉtion andｆｅwer aｍouｎt of veｒticesｏn theｔoｐｏｌｏgicａl ｍap. So, ouｒｓyｓｔeｍhaｓｂｅtter pｅrfoｒmaｎcｅiｎdyｎamｉcｅnｖiｒoｎｍent.Ｔheｓe can be seen in Table 2. Lａｎewaｙs 1, 2, ４, 5 ａｒe ｉn operatioｎｗhere sｏme miners are ｗorｋinｇ, whiｃh puzzle ｔheｒobｏt.

6Ｃonclusions

1) Salｉeｎt locａｌimagｅfeatuｒes are ｅxtrａcted to ｒeplaｃe the whol

ｅｉmagｅto partiｃipａtｅin ｒecogｎition, ｗｈicｈimprｏvｅthｅｔolｅｒance of chanｇeｓin ｓｃalｅ，2Ｄrotaｔｉon and vieｗｐｏiｎt of envirｏnｍｅnｔimage.

２) Ｆｕzzy logic iｓusｅdｔｏrｅcognize the local ｉmage, ａnd eｍphａsize thｅindiｖiｄｕal feａture’s ｃontriｂｕtｉoｎｔｏrｅｃognition, whichｉmproveｓｔhe ｒｅｌiabiliｔｙof lanｄmａrks.

3）ＨMＭｉs usｅｄto ｃaptuｒｅthe ｓtructｕre or ｒeｌationsｈip of tｈose loｃaｌｉｍaｇeｓ，ｗhich cｏnverｔs the scene reｃoｇniｔion proｂlem ｉnto ｔhｅｅvaluation problem of HMＭ.

4）The rｅsuｌts ｆrｏm the abｏve exｐeriｍentｓdｅmoｎstｒatｅt ｈaｔｔhe ｍｉnｅrｅscue roboｔsｃene recognition sysｔｅm ｈas highｅr ratio of recｏｇnition and localization．

Future wｏrk ｗill ｂｅｆoｃused on usiｎg HMM toｄeal witｈｔheｕnｃｅrtainｔy ｏf locａlizａtｉｏｎ.

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1 000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

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英文原文： Building construction concrete crack of prevention and processing Abstract The crack problem of concrete is a widespread existence but again difficult in solve of engineering actual problem, this text carried on a study analysis to a little bit familiar crack problem in the concrete engineering, and aim at concrete the circumstance put forward some prevention, processing measure. Keyword:Concrete crack prevention processing Foreword Concrete's ising 1 kind is anticipate by the freestone bone, cement, water and other mixture but formation of the in addition material of quality brittleness not and all material.Because the concrete construction transform with oneself, control etc. a series problem, harden model of in the concrete existence numerous tiny hole, spirit cave and tiny crack, is exactly because these beginning start blemish of existence just make the concrete present one some not and all the characteristic of quality.The tiny crack is a kind of harmless crack and accept concrete heavy, defend Shen and a little bit other use function not a creation to endanger.But after the concrete be subjected to lotus carry, difference in temperature etc. function, tiny crack would continuously of expand with connect, end formation we can see without the