This　article　proposes　a　tightly　coupled　visual-acoustic　sensor　fusion　method　for　self-localization　of　a　biomimetic　robotic　shark.　To　address　the　decreased　localization　accuracy　of　visual-based　simultaneous　localization　and　mapping　systems　employed　on　a　robotic　fish　in　underwater　environments,　we　integrate　velocity　measurements　from　the　acoustic　sensor　Doppler　velocity　log　(DVL)　into　a　visual　odometry.　To　fully　exploit　the　local　position　change　information　contained　in　velocity　measurements,　DVL　measurements　are　fused　in　two　stages　of　visual　tracking.　Specifically,　we　first　employ　the　velocity　measurements　to　improve　the　initial　camera　pose　estimation　during　visual　tracking,　aiming　to　provide　a　better　initial　value　for　subsequent　pose　optimization.　Thereafter,　these　velocity　measurements　are　directly　employed　to　constrain　the　camera　position　change　between　two　adjacent　frames　by　constructing　a　DVL　residual　term,　which　is　optimized　jointly　with　the　visual　residual　to　obtain　a　more　accurate　camera　pose.　Extensive　experiments　are　conducted　on　both　self-collected　simulated　datasets　and　real-world　underwater　datasets.　Experimental　results　demonstrate　that　the　proposed　visual-acoustic　fusion　method　can　effectively　improve　the　localization　accuracy　for　the　robotic　shark　by　more　than　50%　compared　to　a　pure　visual　system,　providing　valuable　guidance　for　improving　the　autonomous　localization　capability　of　underwater　biomimetic　robots.