The　moving　least　squares　(MLS)　and　moving　total　least　squares　(MTLS)　are　two　of　the　most　popular　methods　used　for　reconstructing　measurement　data,　on　account　of　their　good　local　approximation　accuracy.　However,　their　reconstruction　accuracy　and　robustness　will　be　greatly　reduced　when　there　are　outliers　in　measurement　data.　This　article　proposes　an　improved　MTLS　method　(IMTLS),　which　introduces　an　improved　random　sample　consensus　(RANSAC)　algorithm　and　a　correction　parameter　in　the　support　domain,　to　deal　with　the　outliers　and　random　errors.　Based　on　the　nodes　within　the　support　domain,　firstly　the　improved　RANSAC　is　used　to　generate　a　model　for　establishing　the　group　of　pre-interpolation　and　calculating　the　residual　of　each　node.　Subsequently,　the　abnormal　degree　of　the　node　with　the　largest　residual　is　evaluated　by　the　correction　parameter　associated　with　the　node　residual　and　random　errors.　The　node　with　certain　abnormal　degree　will　be　eliminated　and　the　remaining　nodes　are　used　to　obtain　the　approximation　coefficients.　The　correction　parameter　can　be　used　for　data　reconstruction　without　insufficient　or　excessive　elimination.　The　results　of　numerical　simulation　and　measurement　experiment　show　that　the　reconstruction　accuracy　and　robustness　of　the　IMTLS　method　is　superior　to　the　MLS　and　MTLS　method.