Real-time　3D　mapping　with　MAV　(Micro　Aerial　Vehicle)　in　GPS-denied　environment　is　a　challenging　problem.　In　this　paper,　we　present　an　effective　vision-based　3D　mapping　system　with　2D　laser-scanner.　All　algorithms　necessary　for　this　system　are　on-board.　In　this　system,　two　cameras　work　together　with　the　laser-scanner　for　motion　estimation.　The　distance　of　the　points　detected　by　laser-scanner　are　transformed　and　treated　as　the　depth　of　image　features,　which　improves　the　robustness　and　accuracy　of　the　pose　estimation.　The　output　of　visual　odometry　is　used　as　an　initial　pose　in　the　Iterative　Closest　Point　(ICP)　algorithm　and　the　motion　trajectory　is　optimized　by　the　registration　result.　We　finally　get　the　MAV＇s　state　by　fusing　IMU　with　the　pose　estimation　from　mapping　process.　This　method　maximizes　the　utility　of　the　point　clouds　information　and　overcomes　the　scale　problem　of　lacking　depth　information　in　the　monocular　visual　odometry.　The　results　of　the　experiments　prove　that　this　method　has　good　characteristics　in　real-time　and　accuracy.