In　the　past　few　decades,　visual　SLAM　has　been　successfully　applied　to　technologies　such　as　virtual　reality　and　robot　positioning.　Among　them,　feature　detection　and　matching　technology　is　the　key　technology　in　SLAM.　Aiming　at　the　problems　of　large　scale　matching　error　and　high　mismatch　rate　of　the　binary　description　algorithm　(Oriented　fast　and　Rotated　Brief　(ORB)),　an　improved　ORB　feature　matching　algorithm　in　terms　of　scale　and　descriptors　is　proposed.　Based　on　the　binary　description　algorithm　ORB,　the　algorithm　constructs　a　pyramid-like　scale　space,　and　detects　oFAST　key　points　on　each　layer　to　improve　the　scale　invariance　of　the　algorithm.　In　terms　of　descriptors,　the　128-bit　improved　FREAK　description　operator　is　used　instead　of　the　last　128　bits　of　the　small　variance　in　the　rBRIEF　description　operator,　which　makes　full　use　of　image　information　to　improve　the　matching　accuracy　and　robustness.　The　experimental　results　show　that　the　algorithm　in　this　paper　has　greatly　improved　the　feature　matching　rate　and　robustness　in　terms　of　scale　change,　rotation,　and　brightness　change　compared　with　the　traditional　ORB,　and　meets　the　requirements　for　fast　and　accurate　matching　of　complex　images.　©　2020　IEEE.