This　paper　presents　an　efficient　map　building　technique　for　indoor　mobile　robot　navigation　based　on　laser　range　finder　and　binocular　stereo　vision　sensors.　To　effectively　incorporate　different　sensors　and　deal　with　measurement　uncertainty　involved　in　environment　perception,　this　article　presents　a　local　map　integration　approach　in　which　Bayesian　filter　based　dynamic　occupancy　grid　map　modeling　techniques　are　employed.　The　adopted　method　is　discussed　in　the　context　of　mobile　robot　Simultaneous　Localization　and　Map-Building　(SLAM).　In　SLAM　routine,　the　integrated　local　map　is　utilized　as　observation　input,　and　Rao-Blackwellized　Particle　Filter　(RBPF)　is　used　for　refining　location　estimation　and　generating　accurate　global　map.　Advantages　of　our　proposal　are　validated　by　real　experimental　results　carried　on　Pioneer　robot.　©　2011　IEEE.