Due　to　its　capacity　to　depict　intricate　geometric　shapes　and　topological　structures,　moving　total　least　squares　(MTLS)　method　has　garnered　considerable　attention　from　a　diverse　spectrum　of　researchers,　finding　extensive　utility　in　the　domain　of　reverse　engineering.　Nonetheless,　the　utilization　of　MTLS　becomes　impractical　when　dealing　with　datasets　containing　outliers.　Drawing　inspiration　from　the　construction　principle　of　MTLS,　a　regionally　asymptotic　moving　total　least　squares　(RAMTLS)　approach　is　proposed　to　achieve　robust　reconstruction　of　highly　polluted　measurement　dataset,　in　which　the　Bayesian　estimation　and　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　are　used　for　outlier　detection.　In　contrast　to　the　conventional　method,　the　proposed　approach　employs　a　step-by-step　iterative　strategy　to　mitigate　the　adverse　impact　of　outliers.　Within　this　framework,　Student-t　distribution-based　estimation　with　non-prior　information　is　used　to　pre-fit　the　data　within　the　support　domain,　followed　by　clustering　the　acquired　absolute　residuals.　Upon　the　completion　of　the　regression-clustering　iteration,　a　weighted　Bayesian　estimation　is　further　applied　to　the　remaining　data　to　ascertain　the　ultimate　estimated　value.　Compared　with　existing　competitive　methods,　the　simulations　and　experiments　emphasize　the　effectiveness　and　reliability　of　the　proposed　reconstruction　method.