Highly　accurate　mapping　and　localization　is　of　prime　importance　for　mobile　robotics,　and　its　core　lies　in　efficient　scan　matching.　Previous　research　are　focusing　on　designing　a　robust　objective　function　and　the　residual　error　distribution　is　often　ignored　or　simply　assumed　as　unitary　or　mixture　of　simple　distributions.　In　this　paper,　a　mixture　of　exponential　power　(MoEP)　distributions　is　proposed　to　approximate　the　residual　error　distribution.　The　objective　function　induced　by　MoEP-based　residual　error　modelling　ensembles　a　mix-norm-based　scan　matching　(MiNoM),　which　enhances　the　matching　accuracy　and　convergence　characteristic.　Both　the　parameters　of　transformation　(rotation　and　translation)　and　residual　error　distribution　are　estimated　efficiently　via　an　EM-like　algorithm.　The　optimization　of　MiNoM　is　iteratively　achieved　via　two　phases:　An　on-line　parameter　learning　(OPL)　phase　to　learn　residual　error　distribution　for　better　representation　according　to　the　likelihood　field　model　(LFM),　and　an　iteratively　reweighted　least　squares　(IRLS)　phase　to　attain　transformation　for　accuracy　and　efficiency.　Extensive　experimental　results　validate　that　the　proposed　MiNoM　outperforms　several　state-of-the-art　scan　matching　algorithms　in　both　convergence　characteristic　and　matching　accuracy.