When　the　image　texture　is　simple　and　relatively　indistinct,　it　is　difficult　to　implement　pose　estimation　based　on　point　features.　For　this　problem,　a　visual　SLAM　(simultaneous　localization　and　mapping)　algorithm　based　on　point-line　features　is　proposed　by　using　the　distributed　modular　technology.　Firstly,　the　point　and　line　features　in　the　environment　captured　by　the　camera　are　extracted　and　tracked　according　to　the　inter-frame　feature　matching.　Then,　the　improved　NICP　(normal　iterative　closest　point)　algorithm　and　the　key　frame　matching　strategy　are　used　to　build　the　odometer　system.　Based　on　this,　the　loop　detection　based　on　point-line　feature　dictionary　and　the　graph　optimization　method　of　GTSAM　(Georgia　Tech　smoothing　and　mapping　library)　are　introduced　to　obtain　3D　point　cloud　map　with　globally　consistent　poses.　A　system　framework　is　developed　with　robot　technology　middleware　to　enhance　the　scalability　and　flexibility　of　the　functional　modules　while　improving　the　real-time　performance　of　the　system.　The　experimental　results　on　the　standard　datasets　and　in　laboratory　scenes　verify　the　feasibility　and　effectiveness　of　the　proposed　method.　©　2019,　Science　Press.　All　right　reserved.