Due　to　image　noise,　image　blur,　and　inconsistency　between　depth　data　and　color　image,　the　accuracy　and　robustness　of　the　pairwise　spatial　transformation　computed　by　matching　extracted　features　of　detected　key　points　in　existing　sparse　Red　Green　Blue-Depth　(RGB-D)　Simultaneously　Localization　And　Mapping　(SLAM)　algorithms　are　poor.　Considering　that　most　indoor　environments　follow　the　Manhattan　World　assumption　and　the　Manhattan　Frame　can　be　used　as　a　reference　to　compute　the　pairwise　spatial　transformation,　a　new　RGB-D　SLAM　algorithm　is　proposed.　It　first　performs　the　Manhattan　Frame　Estimation　using　the　introduced　concept　of　orientation　relevance.　Then　the　pairwise　spatial　transformation　between　two　RGB-D　frames　is　computed　with　the　Manhattan　Frame　Estimation.　Finally,　the　Manhattan　Frame　Estimation　using　orientation　relevance　is　incorporated　into　the　RGB-D　SLAM　to　improve　its　performance.　Experimental　results　show　that　the　proposed　RGB-D　SLAM　algorithm　has　definite　improvements　in　accuracy,　robustness,　and　runtime.