Feature-based　visual　SLAM　(vSLAM)　systems　compute　camera　poses　and　scene　maps　by　detecting　and　matching　2D　features,　mostly　being　points　and　line　segments,　from　image　sequences.　These　systems　often　suffer　from　unreliable　detections.　In　this　paper,　we　define　feature　credibility　(FC)　for　both　points　and　line　segments,　formulate　it　into　vSLAMs　and　develop　an　FC-vSLAM　system　based　on　the　widely　used　ORB-SLAM　framework.　Compared　with　existing　credibility　definitions,　the　proposed　one,　considering　both　temporal　observation　stability　and　perspective　triangulation　reliability,　is　more　comprehensive.　We　formulate　the　credibility　in　our　SLAM　system　to　suppress　the　influences　from　unreliable　features　on　the　pose　and　map　optimization.　We　also　present　a　way　to　improve　the　line　end　observations　by　their　multi-view　correspondences,　to　improve　the　integrity　of　the　3D　maps.　Experiments　on　both　the　TUM　and　7-Scenes　datasets　demonstrate　that　our　feature　credibility　and　the　multi-view　line　optimization　are　effective;　the　developed　FC-vSLAM　system　outperforms　existing　popular　feature-based　systems　in　both　localization　and　mapping.