In　this　article,　we　propose　a　tightly　coupled　visual-inertial-acoustic　sensor　fusion　method　to　improve　the　autonomous　localization　accuracy　of　underwater　vehicles.　To　address　the　performance　degradation　encountered　by　existing　visual　or　visual-inertial　simultaneous　localization　and　mapping　systems　when　applied　in　underwater　environments,　we　integrate　the　Doppler　velocity　log　(DVL),　an　acoustic　velocity　sensor,　to　provide　additional　motion　information.　To　fully　leverage　the　complementary　characteristics　among　visual,　inertial,　and　acoustic　sensors,　we　perform　multimodal　information　fusion　in　both　frontend　tracking　and　backend　mapping　processes.　Specifically,　in　the　frontend　tracking　process,　we　first　predict　the　vehicle＇s　pose　using　the　angular　velocity　measurements　from　the　gyroscope　and　linear　velocity　measurements　from　the　DVL.　Thereafter,　measurements　performed　by　the　three　sensors　between　adjacent　camera　frames　are　utilized　to　construct　visual　reprojection　error,　inertial　error,　and　DVL　displacement　error,　which　are　jointly　minimized　to　obtain　a　more　accurate　pose　estimation　at　the　current　frame.　In　the　backend　mapping　process,　we　utilize　gyroscope　and　DVL　measurements　to　construct　relative　pose　change　residuals　between　keyframes,　which　are　minimized　together　with　visual　and　inertial　residuals　to　further　refine　the　poses　of　the　keyframes　within　the　local　map.　Experimental　results　on　both　simulated　and　real-world　underwater　datasets　demonstrate　that　the　proposed　fusion　method　improves　the　localization　accuracy　by　more　than　30%　compared　to　the　current　state-of-the-art　ORB-SLAM3　stereo-inertial　method,　validating　the　potential　of　the　proposed　method　in　practical　underwater　applications.