matlab中英文翻译文献

附录英文原文

Sceｎe ｒeｃognitｉｏn ｆor mｉne ｒesｃue ｒobot ｌocａliｚaｔioｎbaseｄon vision

Abstraｃt:A new ｓceｎe reｃogｎitｉon ｓｙｓｔｅm was presｅnted baｓｅd on fｕzzｙlogiｃａnd hidｄeｎMaｒｋｏv moｄｅｌ(HMＭ) that can be appｌieｄｉn mine rescue roｂoｔlｏcalizａtion duringｅmergencies．The system uses moｎｏcular caｍerａｔo acquire omni-dｉｒectｉonal images of theｍine ｅnvironmｅｎtｗhere thｅroｂoｔlocatｅｓ. Ｂy ａdopting ceｎter-suｒrｏunｄdifferｅｎｃe method, thｅｓalient ｌｏcal ｉmaｇe ｒegｉons arｅextractｅd frｏm tｈe imaｇｅs aｓnatural ｌaｎdmarks. Thｅse ｌａndmarkｓare orｇaniｚed ｂｙusinｇHＭＭto reprｅseｎｔｔhｅscenｅｗhere tｈe roｂot ｉs, ａnｄfuzzy logicｓtrａtegy i ｓusｅｄto maｔch the scene and ｌandｍark. By thisｗay, the ｌocalｉz ａｔioｎpｒｏｂｌem, whｉch iｓthe scene recogniｔion probｌｅｍin tｈe sｙｓteｍ, ｃaｎbe conveｒted into thｅｅvaｌuatiｏn ｐroｂlem of HＭM．Ｔhe cｏntribuｔｉoｎs of these skillｓmake ｔhe syｓtemｈａｖe the aｂｉｌｉty to deal wiｔh chaｎges in scａle, 2Ｄrｏtａtioｎａｎd viewpoint.The rｅｓulｔs of experiｍenｔs aｌｓo prｏve tｈat ｔhｅsｙstｅm hａs highｅrｒatioｏf recognitionａｎd lｏcalｉzationｉｎboth ｓtaticａnd ｄｙnamic mine environｍents.

Keｙwoｒdｓ:roboｔlocａtiｏｎ;ｓcenｅｒecoｇnitiｏn;salieｎt iｍａge; matcｈing ｓｔrategy;ｆｕzzy lｏgic; hiddｅｎMarkov ｍodel

1Inｔrｏｄuction

Seａｒch and rｅscuｅiｎdisasｔer area in tｈe domaiｎof robｏt ｉs a burｇeｏninｇand cｈａllenging subｊecｔ[1]. Minｅreｓcue robot waｓdeｖｅｌｏped to eｎter minｅs duriｎｇemergencｉes to lｏｃate possｉbｌe ｅsｃaｐe routｅs for thｏsｅtrapped insidｅand determｉｎe wｈether it ｉs sa ｆe fｏr ｈumａn to enter or not. Localizａｔioｎis a funｄaｍeｎｔal pｒobleｍin this fiｅld. Loｃalizａtｉon methods based oｎcamera ｃan be mainｌｙclａssified ｉnto geomeｔrｉc, toｐologｉcalｏr hｙbrid onｅs［2]. Wiｔｈｉts feasiｂility and eｆfｅctｉveneｓs，sｃｅｎｅreｃoｇnitiｏn beｃｏmes ｏｎe oｆthe impoｒｔａnt technｏｌogies of topologicａl loｃali ｚａｔion.

Cｕrrently mosｔscｅnｅｒecoｇnｉtioｎmｅthｏds aｒe ｂaｓeｄo ｎgｌoｂａl image feaｔuｒes anｄhaｖe ｔｗo dｉstinｃt stages: traiｎing ｏｆflｉneａnd matcｈinｇonlｉnｅ．

Durｉｎg ｔhe ｔrａining ｓtage, roboｔｃollecｔs the iｍages ｏf the enｖｉrｏｎｍent where it ｗｏrks anｄｐrｏcesseｓtheｉmagｅs to eｘtra ｃｔglｏbａl featuｒes that reｐresentｔhe scene. Some ａpproaｃhｅs ｗｅre usｅd tｏaｎalｙze the datａ-set of image ｄireｃtｌyａnd some prｉm ａry ｆｅatures were fｏuｎd，suｃｈａs tｈｅＰCA mｅthod [3]．However,thｅPCA metｈoｄis noｔｅfｆectivｅin disｔｉｎguisｈingｔhe ｃｌａssｅs of ｆeatureｓ. Anothｅr tｙpｅof apｐroach uｓes appearaｎceｆeatuｒeｓiｎｃludiｎｇcolor, texｔure aｎｄedge ｄensity to represent the image．Ｆｏr eｘaｍple，ＺHOU et aｌ［4] ｕｓed ｍｕltidimensｉonal hiｓｔograms to describe gloｂal aｐpeａrａncｅfeａｔuｒｅｓ.Thｉs methodｉｓsimｐle buｔｓensitive to sｃale aｎd ｉlｌumｉnａｔiｏｎchaｎｇes. Ｉn faｃt,all kｉｎds of globaｌiｍａge featｕrｅs are ｓufferｅdｆrom ｔhe cｈangeｏf enviｒonmｅnt.