Accurate　control　of　continuum　robots　in　confined　environments　presents　a　significant　challenge　due　to　the　need　for　a　precise　kinematic　model,　which　is　susceptible　to　external　interference.　This　paper　introduces　a　model-　less　optimal　visual　control　(MLOVC)　method　that　enables　a　tendon-sheath-driven　continuum　robot　(TSDCR)　to　effectively　track　visual　targets　in　a　confined　environment　while　ensuring　stability.　The　method　allows　for　intraluminal　navigation　of　TSDCRs　along　narrow　lumens.　To　account　for　the　presence　of　external　outliers,　a　robust　Jacobian　estimation　method　is　proposed,　wherein　improved　iterative　reweighted　least　squares　with　sliding　windows　are　used　to　online　calculate　the　robot＇s　Jacobian　matrix　from　sensing　data.　The　estimated　Jacobian　establishes　the　motion　relationship　between　the　visual　feature　and　the　actuation.　Furthermore,　an　optimal　visual　control　method　based　on　quadratic　programming　(QP)　is　designed　for　visual　target　tracking,　while　considering　the　robot＇s　physical　constraint　and　control　constraints.　The　MLOVC　method　for　visual　tracking　provides　a　reliable　alternative　that　does　not　rely　on　the　precise　kinematics　of　TSDCRs　and　takes　into　consideration　the　impact　of　outliers.　The　control　stability　of　the　proposed　approach　is　demonstrated　through　Lyapunov　analysis.　Simulations　and　experiments　are　conducted　to　evaluate　the　effectiveness　of　the　MLOVC　method,　and　the　results　demonstrate　that　it　enhances　tracking　performance　in　terms　of　accuracy　and　stability.