This　paper　proposes　a　registration　approach　rooted　in　point　cloud　clustering　and　segmentation,　named　Clustering　and　Segmentation　Normal　Distribution　Transform　(CSNDT),　with　the　aim　of　improving　the　scope　and　efficiency　of　point　cloud　registration.　Traditional　Normal　Distribution　Transform　(NDT)　algorithms　face　challenges　during　their　initialization　phase,　leading　to　the　loss　of　local　feature　information　and　erroneous　mapping.　To　address　these　limitations,　this　paper　proposes　a　method　of　adaptive　cell　partitioning.　Firstly,　a　judgment　mechanism　is　incorporated　into　the　DBSCAN　algorithm.　This　mechanism　is　based　on　the　standard　deviation　and　correlation　coefficient　of　point　cloud　clusters.　It　improves　the　algorithm＇s　adaptive　clustering　capabilities.　Secondly,　the　point　cloud　is　partitioned　into　straight-line　point　cloud　clusters,　with　each　cluster　generating　adaptive　grid　cells.　These　adaptive　cells　extend　the　range　of　point　cloud　registration.　This　boosts　the　algorithm＇s　robustness　and　provides　an　initial　value　for　subsequent　optimization.　Lastly,　cell　segmentation　is　performed,　where　the　number　of　segments　is　determined　by　the　lengths　of　the　adaptively　generated　cells,　thereby　improving　registration　accuracy.　The　proposed　CSNDT　algorithm　demonstrates　superior　robustness,　precision,　and　matching　efficiency　compared　to　classical　point　cloud　registration　methods　such　as　the　Iterative　Closest　Point　(ICP)　algorithm　and　the　NDT　algorithm.