The　moving　least-squares　(MLS)　and　moving　total　least-squares　(MTLS)　methods　have　been　widely　used　for　fitting　measurement　data.　They　can　be　used　to　achieve　good　approximation　properties.　However,　these　two　methods　are　susceptible　to　outliers　due　to　the　way　of　determining　local　approximate　coefficients,　which　leads　to　distorted　estimation.　To　reduce　the　influence　of　outliers　and　random　errors　of　all　variables　without　adding　small　weights　or　setting　the　threshold　subjectively,　we　present　a　robust　MTLS　(RMTLS)　method,　in　which　an　improved　least　trimmed　squares　(ILTS)　method　is　used　for　obtaining　the　local　approximants　of　the　influence　domain.　The　ILTS　method　divides　the　nodes　in　the　influence　domain　into　a　certain　number　of　subsamples,　achieves　the　local　approximants　by　the　total　least-squares　(TLS)　method　with　compact　support　weight　function,　and　trims　the　node　with　the　largest　orthogonal　residual　from　each　subsample,　respectively.　The　remaining　nodes　from　the　subsamples　are　used　to　determine　the　local　coefficients.　The　measurement　experiment　and　numerical　simulations　are　provided　to　demonstrate　the　robustness　and　accuracy　of　the　presented　method　in　comparison　with　the　MLS　and　MTLS　methods.