Many　methods　have　been　proposed　and　improved　to　deal　with　multi-view　point　cloud　registration.　Most　of　them　are　based　on　the　classical　method,　namely　Iterative　Closest　Point(ICP),　which　is　fast　and　accurate　in　most　cases.　However　in　the　presence　of　noise　and　outliers,　the　equal　weights　ICP　assigned　to　all　correspondences　would　lead　to　unsatisfactory　registration　results.　To　address　this　issue,　this　paper　proposes　a　new　automatic　multi-view　registration　method　based　on　Expectation　Maximization(EM).　A　Gaussian　distribution　on　representing　the　relationship　between　point-sets　according　to　their　distance　was　introduced　first.　Then　EM　is　brought　in　to　optimize　the　likelihood　function　formulated　with　above　Gaussian　distribution.　Through　an　iterative　method　the　multi-view　registration　results　can　be　obtained　at　last.　The　experimental　results　demonstrate　accuracy　and　robustness　of　our　methods　over　four　state-of-the-art　algorithms,　especially　when　noisy　data　exist.