The　Moving　Least　Squares　(MLS)　and　Moving　Total　Least　Squares　(MTLS)　method　are　widely　used　for　approximating　discrete　data　in　many　areas　such　as　surface　reconstruction.　One　of　the　disadvantages　of　MLS　is　that　it　only　considers　the　random　errors　in　the　dependent　variables.　The　MTLS　method　achieves　a　better　fitting　accuracy　by　taking　into　account　the　errors　of　both　dependent　and　independent　variables.　However,　both　MLS　and　MTLS　suffer　from　a　low　fitting　accuracy　when　applied　to　the　measurement　data　with　outliers.　In　this　work,　an　improved　method　named　as　α-MTLS　method　is　proposed,　which　uses　the　Total　Least　Square　(TLS)　method　based　on　singular　value　decomposition　(SVD)　to　fit　the　nodes　in　the　influence　domain　and　introduces　a　geometric　characteristic　parameter　α　to　associated　with　the　abnormal　degree　of　nodes.　The　generated　fitting　points　are　used　to　construct　the　parameter　and　quantify　the　abnormal　degree　of　the　nodes.　The　node　with　the　largest　parameter　value　is　eliminated　and　the　remaining　nodes　are　used　to　determine　the　local　coefficients.　By　trimming　only　one　node　per　influence　domain,　multiple　outliers　of　measurement　data　can　be　effectively　handled.　There　is　no　need　to　set　threshold　values　subjectively　or　assign　weights　which　avoids　the　negative　influence　of　manual　operation.　The　performance　of　the　improved　method　is　demonstrated　by　numerical　simulations　and　measurement　experiment.　It　is　shown　that　the　α-MTLS　method　can　effectively　reduce　the　influence　of　the　outliers　and　thus　has　higher　fitting　accuracy　and　greater　robustness　than　that　of　the　MLS　and　MTLS　method.　©　IMechE　2020.