This　paper　proposes　a　novel　monocular　vision-based　SLAM　(Simultaneous　Localization　and　Mapping)　algorithm　for　mobile　robot.　In　this　proposed　method,　the　tracking　and　mapping　procedures　are　split　into　two　separate　tasks　and　performed　in　parallel　threads.　In　the　tracking　thread,　a　ground　feature-based　pose　estimation　method　is　employed　to　initialize　the　algorithm　for　the　constraint　moving　of　the　mobile　robot.　And　an　initial　map　is　built　by　triangulating　the　matched　features　for　further　tracking　procedure.　In　the　mapping　thread,　an　epipolar　searching　procedure　is　utilized　for　finding　the　matching　features.　A　homography-based　outlier　rejection　method　is　adopted　for　rejecting　the　mismatched　features.　The　indoor　experimental　results　demonstrate　that　the　proposed　algorithm　has　a　great　performance　on　map　building　and　verify　the　feasibility　and　effectiveness　of　the　proposed　algorithm.　©　2016　Songmin　Jia　et　al.