In　this　paper,　a　motion-model-free　monocular　SLAM　algorithm　is　proposed　for　simultaneous　localization　and　mapping　of　a　robotic　system.　A　monocular　image　sequence　captured　by　a　calibrated　camera　is　the　only　input　to　the　system,　and　robust　and　accurate　frame-to-frame　camera　poses　and　a　3D　map　of　the　environment　can　be　estimated　automatically　by　the　approach.　The　pose　estimation　method　takes　advantage　of　the　epipolar　geometry　in　structure　from　motion　(SfM)　to　recover　the　rotation　matrix　and　translation　term　of　the　camera,　and　one　3D　reference　point　is　used　to　recover　the　camera＇s　translation　distance.　Then,　a　random　sampling　consensus　(RANSAC)　framework　is　employed　to　find　the　robust　rotation　matrix　and　translation　vector,　and　a　nonlinear　optimization　algorithm　is　applied　to　optimize　the　estimated　rotation　matrix　and　translation　vector　by　minimizing　the　projection　errors.　Finally,　a　local　bundle　adjustment　algorithm　is　performed　to　optimize　the　results.　Extensive　experimental　evaluations　demonstrate　the　effectiveness　of　the　proposed　monocular　SLAM　algorithm.　©　2014　IEEE.