Addressing　the　challenges　faced　by　existing　point　cloud　registration　methods,　which　are　often　easily　affected　by　noise,　point　cloud　size,　and　initial　pose,　this　study　proposes　a　robust　and　efficient　registration　method　to　enhance　accuracy.　First,　the　original　point　cloud　undergoes　voxel　gridding.　Second,　based　on　the　signature　of　histograms　of　orientation　(SHOT)　feature　descriptor,　a　strengthened　SHOT　keypoint　set　is　extracted　for　rough　matching　using　the　four-point　ensemble　(4PCS)　algorithm.　This　step　achieves　the　initial　registration　of　the　source　and　target　point　clouds.　Finally,　a　dual-scale,　feature-constrained　strategy　is　introduced　to　extend　the　generalized　iterative　closest　point　(ICP)　to　the　voxel　level,　accurately　registering　two　point　clouds　that　have　good　initial　poses.　Herein,　extensive　experiments　are　conducted　on　the　Stanford　public　point　cloud　dataset,　as　well　as　a　real-world　dataset,　to　evaluate　the　applicability　of　the　algorithm　across　various　scenarios.　Experimental　results　show　that　the　point　cloud　registration　algorithm　exhibits　strong　reliability　and　robustness,　effectively　handling　different　initial　positions,　overlap　rates,　and　scales　of　point　clouds.　Moreover,　the　registration　accuracy　in　real-world　scenarios　is　improved　by　37.　6%　and　23.　2%　compared　to　those　corresponding　to　the　ICP　and　fast　four-point　set　(Super-4PCS)　algorithms,　respectively.　©　2025　Universitat　zu　Koln.　All　rights　reserved.