The　visual　SLAM　is　less　dependent　on　hardware,　so　it　attracts　growing　interests.　However,　the　visual　SLAM,　especially　the　Extend　Kalman　Filter-based　monocular　SLAM　is　computational　expensive,　and　is　hard　to　fulfill　real-time　process.　In　this　paper,　we　propose　an　algorithm,　which　uses　the　binary　robust　independent　elementary　Features　descriptor　to　describe　the　features　from　accelerated　segment　test　feature　aiming　at　improving　feature　points　extraction　and　matching,　and　combines　with　the　1-point　random　sample　consensus　strategy　to　speedup　the　EKF-based　visual　SLAM.　The　proposed　algorithm　can　improve　the　robustness　of　the　EKF-based　visual　SLAM　and　make　it　operate　in　real-time.　Experimental　results　validate　the　proposed　algorithm.　©　2013　Springer-Verlag.