Over　the　past　decades,　visual　SLAM　has　successfully　applied　in　robotics　and　augmented　reality.　The　effectiveness　of　the　feature　extraction　has　an　important　influence　on　the　performance　of　the　visual　SLAM.　This　paper　proposes　an　Oriented　AGAST　and　Rotated　BRIEF　(OARB)　method　to　improve　the　efficiency　of　visual　SLAM　to　address　the　specific　application,　such　as　mobile　platform.　We　use　the　AGAST　algorithm　to　detect　corner　points　in　parallel　and　measure　the　direction　of　each　corner.　Then　we　use　the　BRIEF　algorithm　to　calculate　the　descriptor.　We　compare　our　proposed　OARB　method　with　the　ORB　method　in　visual　SLAM　on　two　public　datasets.　Experimental　results　demonstrate　that　our　proposed　OARB　method　can　outperform　the　ORB　method　for　visual　SLAM　in　terms　of　speed　and　meanwhile　achieve　the　competitive　performance.　©　2019　IEEE.