Sampling　is　a　crucial　concern　for　outdoor　light　detection　and　ranging　(LiDAR)　point　cloud　registration　due　to　the　large　amounts　of　point　cloud.　Numerous　algorithms　have　been　devised　to　tackle　this　issue　by　selecting　key　points.　However,　these　approaches　often　necessitate　extensive　computations,　giving　rise　to　challenges　related　to　computational　time　and　complexity.　This　letter　proposes　a　multi-domain　uniform　sampling　method　(MDU-sampling)　for　large-scale　outdoor　LiDAR　point　cloud　registration.　The　feature　extraction　based　on　deep　learning　aggregates　information　from　the　neighbourhood,　so　there　is　redundancy　between　adjacent　features.　The　sampling　method　in　this　paper　is　carried　out　in　the　spatial　and　feature　domains.　First,　uniform　sampling　is　executed　in　the　spatial　domain,　maintaining　local　point　cloud　uniformity.　This　is　believed　to　preserve　more　potential　point　correspondences　and　is　beneficial　for　subsequent　neighbourhood　information　aggregation　and　feature　sampling.　Subsequently,　a　secondary　sampling　in　the　feature　domain　is　performed　to　reduce　redundancy　among　the　features　of　neighbouring　points.　Notably,　only　points　on　the　same　ring　in　LiDAR　data　are　considered　as　neighbouring　points,　eliminating　the　need　for　additional　neighbouring　point　search　and　thereby　speeding　up　processing　rates.　Experimental　results　demonstrate　that　the　approach　enhances　accuracy　and　robustness　compared　with　benchmarks.　The　feature　extraction　based　on　deep　learning　aggregates　information　from　the　neighbourhood,　so　there　is　redundancy　between　adjacent　features.　The　sampling　method　in　this　paper　is　carried　out　in　the　spatial　and　feature　domains,　reducing　the　computational　resources　for　registration.　The　proposed　method　preserves　more　effective　information　compared　to　other　algorithms.　Points　are　only　considered　on　the　same　ring　in　LiDAR　data　as　neighbouring　points,　eliminating　the　need　for　additional　neighbouring　point　search.　This　makes　it　efficient　and　suitable　for　large-scale　outdoor　LiDAR　point　cloud　registration.　image