In　visual　simultaneous　localization　and　mapping　(vSLAM)　systems,　motion　blur　often　leads　to　insufficient　number　of　matched　features,　resulting　in　tracking　failure.　Existing　solutions　often　tackle　this　problem　by　restoring　sharp　images　from　blurry　ones.　However,　the　computational　costs　are　high,　and　the　restored　sharp　images　are　usually　distorted.　The　effect　of　blurry　image　sequences　to　vSLAM　system　is　analyzed,　and　the　relationships　between　feature　matching　and　motion　blur　are　acquired　to　deal　with　the　above　mentioned　problems.　A　local　residual　motion　blur　discrimination　network　is　proposed　to　detect　images　with　motion　blur　efficiently.　Motion　blur　recognition　results　are　coupled　with　a　vSLAM　system　so　that　the　feature　extraction　process　is　guided　by　the　results　from　the　local　residual　motion　blur　discrimination　network.　The　performance　of　the　vSLAM　system　can　be　effectively　enhanced　when　it　is　applied　to　sequences　with　motion　blur.　Experimental　results　on　the　Technische　Universitat　Munchen　dataset　show　that　the　proposed　algorithm　increases　the　average　tracking　length　by　about　200　frames　compared　with　the　original　method　on　some　image　sequences　with　violent　motions.　This　algorithm　effectively　improves　the　stability　and　accuracy　of　the　vSLAM　system.