With(1)　the　rapid　development　of　robots　and　UAVs,　navigation　has　become　a　very　important　topic.　The　combination　of　GPS+IMU　is　undoubtedly　the　standard　of　outdoor　navigation.　Indoor　navigation,　simultaneous　localization　and　mapping　(SLAM)　is　considered　to　be　the　key　to　autonomous　navigation　of　robots　in　unknown　environments.　The　traditional　SLAM　algorithm　based　on　RGB-D　camera　adopts　SIFT　feature　and　needs　GPU　acceleration　to　overcome　the　drawback　of　SIFT　feature　extraction.　In　view　of　the　above　problems,　a　SLAM　algorithm　combining　ORB　features　with　closed　loop　detection　is　proposed.　At　the　front　end,　using　improved　ORB　algorithm　to　speed　up　the　image　feature　point　extraction,　combined　RANSAC　with　ICP　to　calculate　the　position　and　posture　of　the　camera.　In　the　back　end,　closed　loop　detection　algorithm　is　proposed　to　eliminate　the　cumulative　error　of　the　robot,　the　pose　of　the　camera　are　optimized　by　using　graph　optimization　tool　to　obtain　globally　consistent　camera　pose　and　point　cloud.