Rao-Blackwellized　particle　filters　simultaneous　localization　and　mapping　can　yield　effective　results　but　it　has　the　tendency　to　become　inconsistent.　To　ensure　consistency,　a　methodology　of　an　unscented　Kalman　filter　and　Markov　Chain　Monte　Carlo　resampling　are　incorporated.　More　accurate　nonlinear　mean　and　variance　of　the　proposal　distribution　are　obtained　without　the　linearization　procedure　in　extended　Kalman　filter.　Furthermore,　the　particle　impoverishment　induced　by　resampling　is　averted　after　the　resample　move　step.　Thus　particles　are　less　susceptible　to　degeneracies.　The　algorithms　are　evaluated　on　accuracy　and　consistency　using　computer　simulation.　Experimental　results　illustrate　the　advantages　of　our　methods　over　previous　approaches.