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.