With　a　view　to　dealing　with　three　dimensional　(hereafter　referred　to　simply　as　3D)　model　of　indoor　environment　based　on　RGB-D　data,　this　study　proposes　a　method　of　simultaneous　localization　and　mapping　of　the　mobile　sensor.　By　using　the　color　data　and　depth　data　collated　from　the　sensor　to　produce　3D　point　cloud　data　of　each　frame,　and　this　study　uses　siftGPU　to　extract　and　match　the　feature　points　of　the　image.　Furthermore,　the　Random　Sample　Consensus　(hereafter　referred　to　simply　as　RANSAC)　is　used　to　deal　with　the　problem　of　reducing　the　accuracy　of　the　pose　estimation　due　to　the　large　error　of　image　feature　matching,　when　the　motion　is　solved　by　Singular　value　decomposition　(hereafter　referred　to　simply　as　SVD)　which　core　method　is　Iterative　Closest　Point　(hereafter　referred　to　simply　as　ICP)　method.　Finally,　based　on　the　data　obtained　from　the　Loop　closure　with　the　method　of　graph　optimization　to　acquire　the　final　position　and　the　high　precision　point　cloud　map　and　motion　trajectory　are　concatenated.　The　result　of　this　experiment　verifies　the　feasibility　and　effectiveness　of　the　method.