Precise　localization　is　an　essential　issue　for　autonomous　driving　systems.　2D　LiDAR,　as　a　high-precision　sensor,　is　widely　used　in　various　indoor　localization　systems.　However,　in　the　outdoor　environment,　the　existence　of　a　large　number　of　dynamic　targets　makes　the　matching　of　adjacent　point　clouds　particularly　difficult.　Moreover,　the　point　cloud　captured　by　2D　LiDAR　is　sparse,　leading　to　the　localization　accuracy　of　2D　LiDAR　in　the　outdoor　environment　being　very　low　or　even　unable　to　achieve　localization.　Therefore,　an　outdoor　localization　system　fusing　stereo　vision　and　2D　LiDAR　is　proposed.　First,　stereo　vision　is　used　to　calculate　the　relative　pose,　so　as　to　fuse　the　2D　LiDAR　data　in　a　local　time　window　into　a　local　submap.　Then,　the　Dempster-Shafer　evidence　theory　is　used　to　fuse　temporal　information　in　local　submap　to　eliminate　noise　caused　by　dynamic　targets.　Finally,　the　ICA-based　image　matching　method　is　used　to　match　the　local　submap　with　a　pre-constructed　global　prior　map　to　eliminate　the　cumulative　error　of　the　stereo　odometry.　The　experimental　results　on　the　KITTI　dataset　show　that　precise　localization　can　be　achieved　only　using　a　low-cost　stereo　camera　and　2D　LiDAR.　Compared　with　the　odometry　of　ORB-SLAM2,　the　proposed　localization　system　improves　the　localization　performance　by　37.9　%　at　most.　©　2023　Northeast　University.　All　rights　reserved.