This　paper　presents　an　effective　Simultaneous　Localization　and　Map-Building　(SLAM)　technique　for　indoor　mobile　robot　navigation　based　on　laser　scan-matching　and　Rao-Blackwellized　Particle　Filter　(RBPF).　Although　the　Extended　Kalman　Filter　(EKF)　solution　exhibits　some　desirable　properties,　the　associated　geometric　feature　map　itself　fails　to　cope　with　senor　noise　mingled　in　the　incoming　laser　reading　and　unable　to　serve　in　the　environment　absent　of　such　features　as　straight　lines　and　corners.　Compared　with　FastSLAM,　main　advantage　of　our　work　is　the　smart　extension　that　is　made　to　deal　with　sensor　uncertainty　by　using　recursive　Bayesian　updating　based　occupancy　grid　map　management.　Furthermore,　to　improve　the　environment　compatibility,　we　presented　a　dense　laser　scan　matching　approach　which　allows　handling　various　type　of　environment.　Advantages　of　our　proposal　are　validated　by　real　experimental　results　carried　on　Pioneer　robot.　©　2010　IEEE.